8 citations · 23 across the 12 of their papers we have counts for
5 papers · 1 filter
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Sudharshan Suresh, Haozhi Qi, Tingfan Wu +9
To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, suc…
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback
Nathan Lambert, Roberto Calandra
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) more capable in complex settings. RLHF proceeds as collec…
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Ran Wei, Nathan Lambert, Anthony McDonald +2
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilit…
General In-Hand Object Rotation with Vision and Touch
Haozhi Qi, Brent Yi, Sudharshan Suresh +4
We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where…
Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice
Johannes V. S. Busch, Robert Voelckner, Peter Sossalla +3
Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle-to-anything communication allows for the tra…