103 citations · 322 across the 32 of their papers we have counts for
4 papers · 1 filter
Learning Massively Multitask World Models for Continuous Control
Nicklas Hansen, Hao Su, Xiaolong Wang
General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-ta…
Learning to Design Soft Hands using Reward Models
Xueqian Bai, Nicklas Hansen, Adabhav Singh +5
Soft robotic hands promise to provide compliant and safe interaction with objects and environments. However, designing soft hands to be both compliant and functional across diverse…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Adrià López Escoriza, Nicklas Hansen, Stone Tao +2
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively e…