This week's #PaperILike is "Efficient memory-based learning for robot control" (Andrew Moore's dissertation, 1990). This and Moore's follow-up work from the 90s are worth revisiting, especially now that VLAs are starting to remember! PDF: www.cl.cam.ac.uk/techreports/...

This week's #PaperILike is "Learning Montezuma’s Revenge from a Single Demonstration" (Salimans & Chen, 2018). 1 demo + known world model = very natural and still under-explored problem setting. PDF: arxiv.org/abs/1812.03381

This week's #PaperILike is "Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness" (Curtis et al., RSS 2024). State of the art for TAMP + POMDPs. I learn more every time I read this paper. PDF: arxiv.org/abs/2403.10454

This week's #PaperILike is "Empowerment: A Universal Agent-Centric Measure of Control" (Klyubin et al., 2005). An important idea in RL, and a fun read -- mentions bacteria, chimpanzees, Newtonian mechanics, and Othello all within a few sentences. PDF: uhra.herts.ac.uk/id/eprint/28...

This week's #PaperILike is "Integrating Planning and Learning: The PRODIGY Architecture" (Veloso et al., 1995). A foundational project in the history of robot planning + learning, and a good place to look for old ideas that are worth resurfacing. PDF: www.cs.cmu.edu/~jgc/publica...

This week's #PaperILike is "Continuous Deep Q-Learning with Model-based Acceleration" (Gu et al., 2016). Got swept away by other deep RL, but I always liked the idea of parameterizing Q in a form where the optimal policy can be derived analytically. PDF: arxiv.org/abs/1603.00748

This week's #PaperILike is "Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning" (Allen et al., PNAS 2020). Their "Virtual Tools Game" is one I revisit often when brainstorming open challenges. PDF & game: sites.google.com/view/virtual...

This week's #PaperILike is "Robot Task Planning Under Local Observability" (Merlin et al., 2024). LOMDPs are a very natural middle ground between MDPs and POMDPs with enough structure for interesting planning and learning. PDF: maxmerl.in/papers/lomdp...

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

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

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...

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

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

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

This week's #PaperILike is "Sloppy Programming" (Little et al., 2010). 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...

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

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

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

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

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

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 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 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 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...

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

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

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

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...

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...

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

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

This week's #PaperILike is "Learning Value Functions with Relational State Representations for Guiding Task-and-Motion Planning" (Kim & Shimanuki, CoRL 2019). I especially like the focus on *representations* for supporting learning and planning. PDF: proceedings.mlr.press/v100/kim20a/...

This week's #PaperILike is "The Utility of Temporal Abstraction in Reinforcement Learning" (Jong et al., AAMAS 2008). My favorite underrated paper in hierarchical RL. Unpacks how options can help *or hurt* learning performance. Fun writing. PDF: www.ifaamas.org/Proceedings/...

This week's #PaperILike is "Width and Serialization of Classical Planning Problems" (Lipovetzky & Geffner, ECAI 2012). If you only read a few classical planning papers, this should be one! Illuminating and practically useful. PDF: www-i6.informatik.rwth-aachen.de/~hector.geff...

This week's #PaperILike is "Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles" (Budd et al., AAAI 2024). Metareasoning is increasingly important as we continue to make progress on "reasoning." PDF: ojs.aaai.org/index.php/AA...

This week's #PaperILike is "PushWorld: A benchmark for manipulation planning with tools and movable obstacles" (Kansky et al., 2023). Fans of benchmarks like ARC will enjoy the simple mechanics and the difficult reasoning required. PDF: arxiv.org/abs/2301.10289

This week's #PaperILike is "Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning" (Toussaint et al., ICRA 2024). Addresses the meta-reasoning challenge that is core to TAMP. Toussaint is always worth a read. PDF: www.user.tu-berlin.de/mtoussai/24-...

This week's #PaperILike is "The Power of Resets in Online Reinforcement Learning" (Mhammedi et al., 2024). If you're doing RL in sim, why not use the sim to its full potential? Reset to any state! (gym.Env.reset() is not all we need.) PDF: arxiv.org/abs/2404.15417

This week's #PaperILike is "Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments" (Stein et al., CoRL 2018). A highly original combination of learning + planning that is still underrated (despite winning a CoRL award!) PDF: proceedings.mlr.press/v87/stein18a...

This week's #PaperILike is "Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly" (Hartmann et al., TRO 2022). Take two minutes to watch this video: www.youtube.com/watch?v=Gqho... I don't use a lot of emojis, but 🤯 PDF: arxiv.org/abs/2106.02489

This week's #PaperILike is "From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning" (Shah et al., 2024). A fresh & clever approach with very impressive few-shot generalization results. PDF: arxiv.org/abs/2402.11871

This week's #PaperILike is "Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models" (Lamb et al., 2022). Part of an exciting line of work: sites.google.com/view/agent-i... This one has an awesome related work section. PDF: arxiv.org/abs/2207.08229

This week's #PaperILike is "Grounding Language Plans in Demonstrations Through Counterfactual Perturbations" (Wang et al., ICLR 2024). A very ideas-rich paper that combines mode-based planning, LLMs, abstraction, few-shot learning, and real robots! PDF: arxiv.org/abs/2403.17124

🧵1/ New paper! 📄 InnateCoder: Learning Programmatic Options with Foundation Models This is Rubens Moraes' final chapter of his PhD thesis from Universidade Federal de Viçosa, Brazil, in collaboration with Quazi Sadmine and Hendrik Baier. arXiv: arxiv.org/abs/2505.12508

Did something today that I never expected to do: made a donation to Harvard!

This week's #PaperILike is "Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings" (Karia et al., ICAPS 2024). A sophisticated approach to a hard & realistic problem. See also their other nice works on RMDPs. PDF: arxiv.org/abs/2402.08145

This week's #PaperILike is "Meta-Optimization and Program Search using Language Models for Task and Motion Planning" (Shcherba et al., 2025). I don't often post such new papers, but I'm very excited to see more TAMP + LLM-based program synthesis. PDF: arxiv.org/abs/2505.03725

This week's #PaperILike is "Creative Robot Tool Use with Large Language Models" (Xu et al., 2023). Very fun to see real robots solving physics puzzles (with tool use!) PDF: arxiv.org/abs/2310.13065