57 citations · 74 across the 16 of their papers we have counts for
14 papers · 1 filter
RoboICL: Embodied In-Context Learning with GPT-6 Astra
Fangcheng Liu, Yeqing Shen, Anda Cheng +7
General-purpose vision-language models offer a promising way to zero-shot robot control: \gptastra{} excels at open-ended and language- or image-conditioned manipulation but remain…
Imperfection for Precision: Upcycling Imperfect Data for High-Precision Robotic Manipulation
Hao Wei, Yang Liu, Chao Tang +7
Training vision-language-action (VLA) models for high-precision manipulation typically requires task-specific, high-quality data (e.g., teleoperation), which is slow and expensive…
What Matters in Designing World Action Models: An Empirical Study
Chao Tang, Haoqing Wang, Zilang Cen +9
World Action Models (WAMs) have emerged as a promising paradigm for generalizable robot control. Despite the growing number of WAM systems, existing works often introduce unified s…
PSR: Predictive Sensorimotor Representation Learning for Contact-Rich Manipulation
Shengbao Li, Peng Xu, Chao Tang +8
Contact-rich manipulation requires policies to generate precise actions by reasoning over contact forces, robot configurations, and interaction histories beyond visual observations…
An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement
Haojie Shi, Tingguang Li, Qingxu Zhu +3
Learning-based methods have improved locomotion skills of quadruped robots through deep reinforcement learning. However, the sim-to-real gap and low sample efficiency still limit t…
Learning Highly Dynamic Behaviors for Quadrupedal Robots
Chong Zhang, Jiapeng Sheng, Tingguang Li +6
Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversi…
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Haojie Shi, Qingxu Zhu, Lei Han +3
In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which le…
Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals
Tingguang Li, Yizheng Zhang, Chong Zhang +5
In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method c…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
Learning-based Fast Path Planning in Complex Environments
Jianbang Liu, Baopu Li, Tingguang Li +3
In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly fin…
Learning Robot Exploration Strategy with 4D Point-Clouds-like Information as Observations
Zhaoting Li, Tingguang Li, Jiankun Wang +1
Being able to explore unknown environments is a requirement for fully autonomous robots. Many learning-based methods have been proposed to learn an exploration strategy. In the fro…
Learning Hierarchical Control for Robust In-Hand Manipulation
Tingguang Li, Krishnan Srinivasan, Max Qing-Hu Meng +2
Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipula…
Learning to Solve a Rubik's Cube with a Dexterous Hand
Tingguang Li, Weitao Xi, Meng Fang +2
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand. Despite the promising performance of dexterous in-hand manipulation, solving co…
HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots
Tingguang Li, Danny Ho, Chenming Li +3
As one of the most promising areas, mobile robots draw much attention these years. Current work in this field is often evaluated in a few manually designed scenarios, due to the la…