96 citations · 456 across the 88 of their papers we have counts for
14 papers · 1 filter
SkillWrapper: Generative Predicate Invention for Task-level Robot Planning
Ziyi Yang, Benned Hedegaard, Ahmed Jaafar +8
Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic repres…
Learning Parameterized Skills from Demonstrations
Vedant Gupta, Haotian Fu, Calvin Luo +2
We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy…
NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos
Hongyu Li, Lingfeng Sun, Yafei Hu +4
Enabling robots to execute novel manipulation tasks zero-shot is a central goal in robotics. Most existing methods assume in-distribution tasks or rely on fine-tuning with embodime…
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
Rafael Rodriguez-Sanchez, Cameron Allen, George Konidaris
Algorithms that exploit factored Markov decision processes are far more sample-efficient than factor-agnostic methods, yet they assume a factored representation is known a priori -…
Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains
Ruo Yu Tao, Kaicheng Guo, Cameron Allen +1
Mitigating partial observability is a necessary but challenging task for general reinforcement learning algorithms. To improve an algorithm's ability to mitigate partial observabil…
Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces
Saket Tiwari, Omer Gottesman, George Konidaris
Advances in reinforcement learning (RL) have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most th…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…
Accelerating Residual Reinforcement Learning with Uncertainty Estimation
Lakshita Dodeja, Karl Schmeckpeper, Shivam Vats +4
Residual Reinforcement Learning (RL) is a popular approach for adapting pretrained policies by learning a lightweight residual policy that provides corrective actions. While Residu…
Optimal Interactive Learning on the Job via Facility Location Planning
Shivam Vats, Michelle Zhao, Patrick Callaghan +4
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 e…
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
Benned Hedegaard, Yichen Wei, Ziyi Yang +4
Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems. Although TAMP methods have historically assumed that each task-leve…
Knowledge Retention for Continual Model-Based Reinforcement Learning
Yixiang Sun, Haotian Fu, Michael Littman +1
We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that d…
Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network
Vincent Hsiao, Mark Roberts, Laura M. Hiatt +2
A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Env…
V-HOP: Visuo-Haptic 6D Object Pose Tracking
Hongyu Li, Mingxi Jia, Tuluhan Akbulut +3
Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this mul…
Visual Theory of Mind Enables the Invention of Proto-Writing
Benjamin A. Spiegel, Lucas Gelfond, George Konidaris
Symbolic writing systems are graphical semiotic codes that are ubiquitous in modern society but are otherwise absent in the animal kingdom. Anthropological evidence suggests that t…