7 papers
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation
Xiangtong Yao, Hongkuan Zhou, Oier Mees +12
Language-conditioned robot manipulation is an emerging field aimed at enabling seamless communication and cooperation between humans and robotic agents by teaching robots to compre…
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
Cheng Qian, Ruomeng Fan, Yifei Ren +2
In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without…
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…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
R+X: Retrieval and Execution from Everyday Human Videos
Georgios Papagiannis, Norman Di Palo, Pietro Vitiello +1
We present R+X, a framework which enables robots to learn skills from long, unlabelled, first-person videos of humans performing everyday tasks. Given a language command from a hum…