Tom Silver
@tomssilver.bsky.social
Assistant Professor @Princeton. Developing robots that plan and learn to help people. Prev: @Cornell, @MIT, @Harvard.
https://tomsilver.github.io/
https://tomsilver.github.io/
This week's #PaperILike is "Bayesian Residual Policy Optimization" (Lee et al., 2020).
I like this POMDP approach because it reduces the problem to figuring out a good set of "clairvoyant experts".
PDF: arxiv.org/abs/2002.03042
I like this POMDP approach because it reduces the problem to figuring out a good set of "clairvoyant experts".
PDF: arxiv.org/abs/2002.03042
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as Bayesian Reinforcement Learning over latent Mar...
arxiv.org
January 18, 2026 at 1:54 PM
This week's #PaperILike is "Bayesian Residual Policy Optimization" (Lee et al., 2020).
I like this POMDP approach because it reduces the problem to figuring out a good set of "clairvoyant experts".
PDF: arxiv.org/abs/2002.03042
I like this POMDP approach because it reduces the problem to figuring out a good set of "clairvoyant experts".
PDF: arxiv.org/abs/2002.03042
This week's #PaperILike is "Differentiable GPU-Parallelized Task and Motion Planning" (Shen et al., RSS 2025).
As always, meticulous work from @WillShenSaysHi and team. TAMP + GPU is long overdue!
PDF: arxiv.org/abs/2411.11833
As always, meticulous work from @WillShenSaysHi and team. TAMP + GPU is long overdue!
PDF: arxiv.org/abs/2411.11833
Differentiable GPU-Parallelized Task and Motion Planning
Planning long-horizon robot manipulation requires making discrete decisions about which objects to interact with and continuous decisions about how to interact with them. A robot planner must select g...
arxiv.org
January 11, 2026 at 2:38 PM
This week's #PaperILike is "Differentiable GPU-Parallelized Task and Motion Planning" (Shen et al., RSS 2025).
As always, meticulous work from @WillShenSaysHi and team. TAMP + GPU is long overdue!
PDF: arxiv.org/abs/2411.11833
As always, meticulous work from @WillShenSaysHi and team. TAMP + GPU is long overdue!
PDF: arxiv.org/abs/2411.11833
Continuing my pace of writing a new blog post every 2 years of so, here's the latest: "Prompt Fiddling Considered Harmful"
tomsilver.github.io/blog/2026/pr...
tomsilver.github.io/blog/2026/pr...
Tom Silver | Prompt Fiddling Considered Harmful
Tom Silver's academic website.
tomsilver.github.io
January 7, 2026 at 1:27 PM
Continuing my pace of writing a new blog post every 2 years of so, here's the latest: "Prompt Fiddling Considered Harmful"
tomsilver.github.io/blog/2026/pr...
tomsilver.github.io/blog/2026/pr...
This week's #PaperILike is "Kinodynamic Task and Motion Planning using VLM-guided and Interleaved Sampling" (Kwon & Kim, 2025).
I particularly like using VLMs to guide backtracking in TAMP. Outperforms PDDLStream and LLM3.
PDF: arxiv.org/abs/2510.26139
I particularly like using VLMs to guide backtracking in TAMP. Outperforms PDDLStream and LLM3.
PDF: arxiv.org/abs/2510.26139
Kinodynamic Task and Motion Planning using VLM-guided and Interleaved Sampling
Task and Motion Planning (TAMP) integrates high-level task planning with low-level motion feasibility, but existing methods are costly in long-horizon problems due to excessive motion sampling. While ...
arxiv.org
January 4, 2026 at 3:09 PM
This week's #PaperILike is "Kinodynamic Task and Motion Planning using VLM-guided and Interleaved Sampling" (Kwon & Kim, 2025).
I particularly like using VLMs to guide backtracking in TAMP. Outperforms PDDLStream and LLM3.
PDF: arxiv.org/abs/2510.26139
I particularly like using VLMs to guide backtracking in TAMP. Outperforms PDDLStream and LLM3.
PDF: arxiv.org/abs/2510.26139
This week's #PaperILike is "Learning Exploration Strategies to Solve Real-World Marble Runs" (Allaire & Atkeson, ICRA 2023).
A very fun and creative challenge for robot physical reasoning.
Video: sites.google.com/view/learnin...
PDF: arxiv.org/abs/2303.04928
A very fun and creative challenge for robot physical reasoning.
Video: sites.google.com/view/learnin...
PDF: arxiv.org/abs/2303.04928
Home
Learning Exploration Strategies to Solve Real-World Marble Runs
sites.google.com
December 28, 2025 at 4:09 PM
This week's #PaperILike is "Learning Exploration Strategies to Solve Real-World Marble Runs" (Allaire & Atkeson, ICRA 2023).
A very fun and creative challenge for robot physical reasoning.
Video: sites.google.com/view/learnin...
PDF: arxiv.org/abs/2303.04928
A very fun and creative challenge for robot physical reasoning.
Video: sites.google.com/view/learnin...
PDF: arxiv.org/abs/2303.04928
This week's #PaperILike is "Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States" (Adu-Bredu, RAL 2022).
Love the focus on planning with "low entropy beliefs" -- not full-fledged POMDPs, but also not full observability.
PDF: arxiv.org/abs/2011.09105
Love the focus on planning with "low entropy beliefs" -- not full-fledged POMDPs, but also not full observability.
PDF: arxiv.org/abs/2011.09105
Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States
Recent advances in computational perception have significantly improved the ability of autonomous robots to perform state estimation with low entropy. Such advances motivate a reconsideration of robot...
arxiv.org
December 21, 2025 at 1:59 PM
This week's #PaperILike is "Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States" (Adu-Bredu, RAL 2022).
Love the focus on planning with "low entropy beliefs" -- not full-fledged POMDPs, but also not full observability.
PDF: arxiv.org/abs/2011.09105
Love the focus on planning with "low entropy beliefs" -- not full-fledged POMDPs, but also not full observability.
PDF: arxiv.org/abs/2011.09105
This week's #PaperILike is "Sloppy Programming" (Little et al., 2010).
Vibe coding before it was cool.
PDF: dspace.mit.edu/bitstream/ha...
Vibe coding before it was cool.
PDF: dspace.mit.edu/bitstream/ha...
dspace.mit.edu
December 14, 2025 at 1:11 PM
This week's #PaperILike is "Sloppy Programming" (Little et al., 2010).
Vibe coding before it was cool.
PDF: dspace.mit.edu/bitstream/ha...
Vibe coding before it was cool.
PDF: dspace.mit.edu/bitstream/ha...
This week's #PaperILike is "Robot Programming" (Tomas Lozano-Perez, 1983).
A prescient paper that asks how we might generally program robots like we program computers. Much remains true 42 years later.
PDF: homes.cs.washington.edu/~ztatlock/59...
A prescient paper that asks how we might generally program robots like we program computers. Much remains true 42 years later.
PDF: homes.cs.washington.edu/~ztatlock/59...
homes.cs.washington.edu
December 7, 2025 at 12:51 PM
This week's #PaperILike is "Robot Programming" (Tomas Lozano-Perez, 1983).
A prescient paper that asks how we might generally program robots like we program computers. Much remains true 42 years later.
PDF: homes.cs.washington.edu/~ztatlock/59...
A prescient paper that asks how we might generally program robots like we program computers. Much remains true 42 years later.
PDF: homes.cs.washington.edu/~ztatlock/59...
This week's #PaperILike is "Learning Proofs of Motion Planning Infeasibility" (Li & Dantam, RSS 2021).
I like using learning to "fail fast", with guarantees. Important for TAMP, where there are other MP problems to try next.
PDF: www.roboticsproceedings.org/rss17/p064.pdf
I like using learning to "fail fast", with guarantees. Important for TAMP, where there are other MP problems to try next.
PDF: www.roboticsproceedings.org/rss17/p064.pdf
www.roboticsproceedings.org
November 30, 2025 at 2:53 PM
This week's #PaperILike is "Learning Proofs of Motion Planning Infeasibility" (Li & Dantam, RSS 2021).
I like using learning to "fail fast", with guarantees. Important for TAMP, where there are other MP problems to try next.
PDF: www.roboticsproceedings.org/rss17/p064.pdf
I like using learning to "fail fast", with guarantees. Important for TAMP, where there are other MP problems to try next.
PDF: www.roboticsproceedings.org/rss17/p064.pdf
This week's #PaperILike is "Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter" (Huang et al., ICRA 2022).
Impressive results on a difficult and subtle problem, with a nice combo of planning + learning.
PDF: arxiv.org/abs/2202.01426
Impressive results on a difficult and subtle problem, with a nice combo of planning + learning.
PDF: arxiv.org/abs/2202.01426
Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter
In this study, working with the task of object retrieval in clutter, we have developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable a Deep Neural Netwo...
arxiv.org
November 23, 2025 at 4:20 PM
This week's #PaperILike is "Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter" (Huang et al., ICRA 2022).
Impressive results on a difficult and subtle problem, with a nice combo of planning + learning.
PDF: arxiv.org/abs/2202.01426
Impressive results on a difficult and subtle problem, with a nice combo of planning + learning.
PDF: arxiv.org/abs/2202.01426
This week's #PaperILike is "Goal-Oriented End-User Programming of Robots" (Porfirio et al., HRI 2024).
I like this use of planning to fill in the gaps between subgoals that are directly programmed by end users.
PDF: arxiv.org/abs/2403.13988
I like this use of planning to fill in the gaps between subgoals that are directly programmed by end users.
PDF: arxiv.org/abs/2403.13988
Goal-Oriented End-User Programming of Robots
End-user programming (EUP) tools must balance user control with the robot's ability to plan and act autonomously. Many existing task-oriented EUP tools enforce a specific level of control, e.g., by re...
arxiv.org
November 16, 2025 at 2:14 PM
This week's #PaperILike is "Goal-Oriented End-User Programming of Robots" (Porfirio et al., HRI 2024).
I like this use of planning to fill in the gaps between subgoals that are directly programmed by end users.
PDF: arxiv.org/abs/2403.13988
I like this use of planning to fill in the gaps between subgoals that are directly programmed by end users.
PDF: arxiv.org/abs/2403.13988
This week's #PaperILike is "Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models" (Tziafas & Kasaei, ICRA 2024).
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models
Large Language Models (LLMs) have emerged as a new paradigm for embodied reasoning and control, most recently by generating robot policy code that utilizes a custom library of vision and control primi...
arxiv.org
November 9, 2025 at 1:52 PM
This week's #PaperILike is "Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models" (Tziafas & Kasaei, ICRA 2024).
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
DreamCoder-like robot skill learning. Refactoring helps!
PDF: arxiv.org/abs/2406.18746
Happy to share some of the first work from my new lab! This project has shaped my thinking about how we can effectively combine planning and RL. Key idea: start with a planner that is slow and "robotic", then use RL to discover shortcuts that are fast and dynamic. (1/2)
Robots can plan, but rarely improvise. How do we move beyond pick-and-place to multi-object, improvisational manipulation without giving up completeness guarantees?
(1/8)
(1/8)
November 5, 2025 at 3:22 PM
Happy to share some of the first work from my new lab! This project has shaped my thinking about how we can effectively combine planning and RL. Key idea: start with a planner that is slow and "robotic", then use RL to discover shortcuts that are fast and dynamic. (1/2)
This week's #PaperILike is "Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems" (Riviere et al., Science Robotics 2024).
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems
The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training...
arxiv.org
November 2, 2025 at 1:27 PM
This week's #PaperILike is "Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems" (Riviere et al., Science Robotics 2024).
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
A creative synthesis of control theory and search. I like using the Gramian to branch.
PDF: arxiv.org/abs/2412.11270
This week's #PaperILike is "Reality Promises: Virtual-Physical Decoupling Illusions in Mixed Reality via Invisible Mobile Robots" (Kari & Abtahi, UIST 2025).
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
Reality Promises
mkari.de
October 26, 2025 at 3:02 PM
This week's #PaperILike is "Reality Promises: Virtual-Physical Decoupling Illusions in Mixed Reality via Invisible Mobile Robots" (Kari & Abtahi, UIST 2025).
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This is some Tony Stark level stuff! XR + robots = future.
Website: mkari.de/reality-prom...
PDF: mkari.de/reality-prom...
This week's #PaperILike is "Learning to Guide Task and Motion Planning Using Score-Space Representation" (Kim et al., IJRR 2019).
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to ...
arxiv.org
October 19, 2025 at 3:02 PM
This week's #PaperILike is "Learning to Guide Task and Motion Planning Using Score-Space Representation" (Kim et al., IJRR 2019).
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This is one of those papers that I return to over the years and appreciate more every time. Chock full of ideas.
PDF: arxiv.org/abs/1807.09962
This week's #PaperILike is "On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills" (Han et al., CoRL 2023).
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practic...
arxiv.org
October 12, 2025 at 12:38 PM
This week's #PaperILike is "On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills" (Han et al., CoRL 2023).
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This and others have convinced me that I need to learn Koopman! Another perspective on abstraction learning.
PDF: arxiv.org/abs/2303.13446
This week's #PaperILike is "Predictive Representations of State" (Littman et al., 2001).
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
web.eecs.umich.edu
October 5, 2025 at 2:45 PM
This week's #PaperILike is "Predictive Representations of State" (Littman et al., 2001).
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
A lesser known classic that is overdue for a revival. Fans of POMDPs will enjoy.
PDF: web.eecs.umich.edu/~baveja/Pape...
This week's #PaperILike is "Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems" (Suau et al., ICML 2022).
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which t...
arxiv.org
September 28, 2025 at 12:18 PM
This week's #PaperILike is "Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems" (Suau et al., ICML 2022).
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
Nice work on using fast local simulators to plan & learn in large partially observed worlds.
PDF: arxiv.org/abs/2202.01534
This week's #PaperILike is "Optimal Interactive Learning on the Job via Facility Location Planning" (Vats et al., RSS 2025).
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
Optimal Interactive Learning on the Job via Facility Location Planning
Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human effort, they are typi...
arxiv.org
September 21, 2025 at 3:44 PM
This week's #PaperILike is "Optimal Interactive Learning on the Job via Facility Location Planning" (Vats et al., RSS 2025).
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
I always enjoy a surprising connection between one problem (COIL) and another (UFL). And I always like work by Shivam Vats!
PDF: arxiv.org/abs/2505.00490
This week's #PaperILike is "Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models" (Shi et al., ICML 2025).
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
Generalist robots that can perform a range of different tasks in open-world settings must be able to not only reason about the steps needed to accomplish their goals, but also process complex instruct...
arxiv.org
September 14, 2025 at 3:37 PM
This week's #PaperILike is "Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models" (Shi et al., ICML 2025).
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
I'm often asked: how might we combine ideas from hierarchical planning and VLAs? This is a good start!
PDF: arxiv.org/abs/2502.19417
This week's #PaperILike is "Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming" (Bonet & Geffner, 2003).
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
ftp.cs.ucla.edu
September 7, 2025 at 2:07 PM
This week's #PaperILike is "Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming" (Bonet & Geffner, 2003).
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
A very clear introduction to and improvement of RTDP, an online MDP planner that we should all have in our toolkits.
PDF: ftp.cs.ucla.edu/pub/stat_ser...
This week's #PaperILike is "Learning and Executing Generalized Robot Plans" (Fikes et al., 1972).
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Shakey: Experiments in Robot Planning and Learning (1972)
YouTube video by Stanford University Libraries
www.youtube.com
August 31, 2025 at 2:51 PM
This week's #PaperILike is "Learning and Executing Generalized Robot Plans" (Fikes et al., 1972).
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
Classic early work on learning & planning from the team behind STRIPS, A* search, and Shakey the robot (www.youtube.com/watch?v=GmU7...).
PDF: stacks.stanford.edu/file/druid:c...
This week's #PaperILike is "Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing" (Xue et al., 2024).
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing
Due to high dimensionality and non-convexity, real-time optimal control using full-order dynamics models for legged robots is challenging. Therefore, Nonlinear Model Predictive Control (NMPC) approach...
arxiv.org
August 24, 2025 at 3:16 PM
This week's #PaperILike is "Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing" (Xue et al., 2024).
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
My favorite part is the clear running example in 2D (Fig 2 & 4). I want examples like this in my papers!
PDF: arxiv.org/abs/2409.15610
This week's #PaperILike is "Abstraction Refinement-guided Program Synthesis for Robot Learning from Demonstrations" (Cui et al., 2025).
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
herowanzhu.github.io
August 17, 2025 at 4:46 PM
This week's #PaperILike is "Abstraction Refinement-guided Program Synthesis for Robot Learning from Demonstrations" (Cui et al., 2025).
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf
And other recent papers by the same group---exciting progress in programmatic RL with applications to robotics.
PDF: herowanzhu.github.io/roboscribe.pdf