103 citations · 168 across the 11 of their papers we have counts for
13 papers
ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim
Ivan Kapelyukh, Xiaohan Zhang, Stephen James +2
We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demonstrations, zero policy pre-tra…
Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim
Ivan Kapelyukh, Yafei Hu, Ran Gong +6
Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction. We introduce Simify, a train…
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…
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…
One-Shot Dual-Arm Imitation Learning
Yilong Wang, Edward Johns
We introduce One-Shot Dual-Arm Imitation Learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task…
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…
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
Norman Di Palo, Edward Johns
We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action se…
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models
Norman Di Palo, Edward Johns
We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Tran…
On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
Norman Di Palo, Edward Johns
Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end behavioural cloning methods. In this paper, we explore an alternative paradigm…
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…
Language Models as Zero-Shot Trajectory Generators
Teyun Kwon, Norman Di Palo, Edward Johns
Large Language Models (LLMs) have recently shown promise as high-level planners for robots when given access to a selection of low-level skills. However, it is often assumed that L…
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…