This week's #PaperILike is "Algorithm Runtime Prediction" (Hutter et al., 2014). "I don't know the answer, but I know I will know the answer in about 10 minutes" (e.g., to a SAT question). Pretty wild that this is possible! PDF: arxiv.org/abs/1211.0906

Algorithm Runtime Prediction: Methods & Evaluation

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a ...

arxiv.org

This week's #PaperILike is "A general scenario theory for non-convex optimization and decision making" (Campi et al., 2018). Very general idea with applications in robotics, e.g., Safe Horizon MPC (arxiv.org/abs/2307.01070) PDF: re.public.polimi.it/retrieve/e0c...

This week's #PaperILike is "A micro Lie theory for state estimation in robotics" (Solà, Deray, & Atchuthan, 2018). Pay the small tax of learning a little Lie theory -- it clarifies many robotics concepts (and much code!). PDF: arxiv.org/abs/1812.01537

A micro Lie theory for state estimation in robotics

A Lie group is an old mathematical abstract object dating back to the XIX century, when mathematician Sophus Lie laid the foundations of the theory of continuous transformation groups. As it often hap...

arxiv.org

This week's #PaperILike is "Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning" (Xiao et al., RSS 2026). Really impressive open-world long-horizon mobile manipulation examples: open-world-planning.github.io PDF: arxiv.org/abs/2607.06501

This week's #PaperILike is "General agents contain world models" (Richens et al., ICML 2025). Brought to my attention by @dabelcs.bsky.social 's beautiful philosophy-laden talk at ICAPS. The explicit construction of a transition model from a policy is cool (Alg 1). PDF: arxiv.org/abs/2506.01622

General agents contain world models

Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of gene...

arxiv.org

This week's #PaperILike is "Linearly-solvable Markov decision problems" (Todorov 2006). MDP 101: Bellman eq for given pi is linear; for optimal value is nonlinear b/c of "max". But: there's a subclass of MDPs where optimal Bellman eq *is* linear! PDF: proceedings.neurips.cc/paper_files/...

This week's #PaperILike is "Robust Planning for Multi-stage Forceful Manipulation" (Holladay et al., IJRR 2023). Sec. 6 is the clearest self-contained intro to PDDLStream I've seen. Also: robots with knives and child-proof bottles! (Very impressive) PDF: arxiv.org/abs/2208.00319

Robust Planning for Multi-stage Forceful Manipulation

Multi-step forceful manipulation tasks, such as opening a push-and-twist childproof bottle, require a robot to make various planning choices that are substantially impacted by the requirement to exert...

arxiv.org

This week's #PaperILike is "Multi-modal motion planning in non-expansive spaces" (Hauser & Latombe, IJRR 2010). I learn more on every re-read of this foundational paper. Also appreciate the beautiful manifold figures! PDF: ai.stanford.edu/~latombe/pap...

This week's #PaperILike is "Using Memory-Based Learning to Solve Tasks with State-Action Constraints" (Verghese & Atkeson, ICRA 2023). Love the real-robot physical puzzle in this one. Video of puzzle: www.youtube.com/watch?v=EzPe... PDF: arxiv.org/abs/2303.04327

Using Memory-Based Learning to Solve Tasks with State-Action Constraints

YouTube video by Mrinal Verghese

youtube.com

This week's #PaperILike is "Plan-based Reward Shaping for Reinforcement Learning" (Grzes & Kudenko, 2008). A nice combo of planning and RL that takes seriously the policy invariance ideas from Ng, Harada, & Russell (1999) [another paper I like!]. PDF: www.cs.kent.ac.uk/people/staff...

This week's #PaperILike is "QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?" (Li, Kim, & Wang, NeurIPS 2025). Beautiful paper & increasingly important as agents start to ask more Qs. Curious how SOTA models do. PDF: arxiv.org/abs/2503.22674

QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?

Large language models (LLMs) have shown impressive performance on reasoning benchmarks like math and logic. While many works have largely assumed well-defined tasks, real-world queries are often under...

arxiv.org

This week's #PaperILike is "Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary" (Asai & Fukunaga, AAAI 2018). One of the key papers that got me hooked on learning + planning before I started grad school. PDF: arxiv.org/abs/1705.00154

Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning h...

arxiv.org

This week's #PaperILike is "Human-Guided Complexity-Controlled Abstractions" (Peng et al., NeurIPS 2023). Selecting the right levels and kinds of abstractions remains important and open for many forms of human-AI / human-robot interaction. PDF: arxiv.org/abs/2310.17550

Human-Guided Complexity-Controlled Abstractions

Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concepts or words) at a va...

arxiv.org

This week's #PaperILike is "HG-DAgger: Interactive Imitation Learning with Human Experts" (Kelly et al., 2019). This would definitely be high on my list of "papers to read if you want to understand what robot foundation model startups are doing." PDF: arxiv.org/abs/1810.02890

HG-DAgger: Interactive Imitation Learning with Human Experts

Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these ...

arxiv.org

This week's #PaperILike is "Motions in Microseconds via Vectorized Sampling-Based Planning" (Thomason et al., ICRA 2024). Incredibly fast motion planner. Fun demo: zkingston.com/vamp-web/ Code: github.com/KavrakiLab/v... PDF: arxiv.org/abs/2309.14545

This week's #PaperILike is "Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators" (Yoon et al., ICRA 2023). I had an idea, looked for related work, found it had already been done, but better! PDF: sgvr.kaist.ac.kr/~msyoon/pape...

This week's #PaperILike is "INQUIRE: INteractive Querying for User-aware Informative REasoning" (Fitzgerald et al., CoRL 2022). A very nice way to unify different forms of information gathering and preference learning for human-robot assistance. PDF: proceedings.mlr.press/v205/fitzger...

This week's #PaperILike is "AssistanceZero: Scalably Solving Assistance Games" (Laidlaw et al., ICML 2025). AlphaZero-like combo of learning & planning for assistance games, where robot & human share reward fn that robot doesn't know. + Minecraft! PDF: arxiv.org/abs/2504.07091

AssistanceZero: Scalably Solving Assistance Games

Assistance games are a promising alternative to reinforcement learning from human feedback (RLHF) for training AI assistants. Assistance games resolve key drawbacks of RLHF, such as incentives for dec...

arxiv.org

This week's #PaperILike is "Cooperative Inverse Reinforcement Learning" (Hadfield-Menell et al., 2016). Seminal work. My favorite part is the simple example showing that demonstration-by-expert is suboptimal. PDF: arxiv.org/abs/1606.03137

Cooperative Inverse Reinforcement Learning

For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to...

arxiv.org

This week's #PaperILike is "RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools" (Shi et al., CoRL 2023). So much to like in one paper: planning, learning, deformable manipulation, GNNs, 15 3D-printed tools, and dumplings! PDF: arxiv.org/abs/2306.14447

RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools

Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark ...

arxiv.org

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

Learning Montezuma's Revenge from a Single Demonstration

We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge. Instead of imitating human demonstrations, as proposed in othe...

arxiv.org

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

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness

Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAMP problem formulatio...

arxiv.org

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

Continuous Deep Q-Learning with Model-based Acceleration

Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However,...

arxiv.org

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...

virtualtools

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, ...

sites.google.com

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

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

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

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