activity
20162025
most citedOnline Contrastive Divergence with Generative Replay: Experience Replay without Storing Data

18 citations · 42 across the 5 of their papers we have counts for

collaborators
Showing 2023Show all

16 papers · 1 filter

cs.RO2023

ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Home

Sveta Paster, Kantwon Rogers, Gordon Briggs +2

With the projected surge in the elderly population, service robots offer a promising avenue to enhance their well-being in elderly care homes. Such robots will encounter complex sc…

cs.RO20231 cited

Exploring the Cost of Interruptions in Human-Robot Teaming

Swathi Mannem, William Macke, Peter Stone +1

Productive and efficient human-robot teaming is a highly desirable ability in service robots, yet there is a fundamental trade-off that a robot needs to consider in such tasks. On…

cs.RO2023

Learning Generalizable Manipulation Policies with Object-Centric 3D Representations

Yifeng Zhu, Zhenyu Jiang, Peter Stone +1

We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors. GROOT builds policies that generalize beyond their initial training…

cs.RO20232 cited

STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience

Haresh Karnan, Elvin Yang, Daniel Farkash +3

Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigatio…

cs.LG20231 cited

ELDEN: Exploration via Local Dependencies

Jiaheng Hu, Zizhao Wang, Peter Stone +1

Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement learning. In these tasks, an agent needs to explore the state space efficiently un…

cs.LG20231 cited

-Policy Gradients: A General Framework for Goal Conditioned RL using -Divergences

Siddhant Agarwal, Ishan Durugkar, Peter Stone +1

Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy…