629.8
Robotics & Embodiment
Machines that sense, move and manipulate.
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robot manipulation policy learning
humanoid robot locomotion
629.8
Machines that sense, move and manipulate.
Drawer contents
Filled from
Search threads
robot manipulation policy learning
humanoid robot locomotion
JoyAI-RA Team
Robot data is scarce, so generalist policies need to learn from heterogeneous sources, including human egocentric video, simulation, and real robots, which differ in supervision and embodiment, with action labels missing or mutually incompatible. Human egocentric data scale best but sit farthest from robot data, and…
He Kong, Zengjue Chen, Qi Wang, Qianli Xing + 5 more
Vision-language-action (VLA) models have demonstrated remarkable capabilities in robotic manipulation by leveraging pretrained vision-language models. However, existing post-training methods predominantly optimize VLA models as flat policies, making it difficult to explicitly model task progression and perform robust…
Chenghao Gu, Hanyang Yu, Jingbo Zhang, Haitao Lin + 6 more
Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning and evaluation in diverse real-world environments remains costly and challenging. Action-conditioned world models offer a promising alternative, but they often suffer…
Zhe Li, Zhenzhe Zhang, Yangyang Wei, Wenjie Zhang + 7 more
Humanoid household tasks often require concurrent loco-manipulation, where the robot must move, adjust posture, maintain balance, and manipulate objects as a single coordinated behavior. Yet existing humanoid policies typically decompose locomotion and manipulation, while recent world-action models remain either…
Hisham Khalil, Neil Fernandes, Thomas M. Kwok, Hsiu-Chin Lin + 1 more
Contact-rich manipulation requires precise tracking and mechanical compliance, where variable impedance control can improve robustness in task success, whereas static compliance cannot adapt to varying contact constraints. Variable impedance skills can be learned from demonstrations, avoiding complex modeling, but…
Peiyan Li, Yuze Zhu, Yixiang Chen, Qisen Ma + 9 more
Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot manipulation. However, existing 3D VLA methods remain data-hungry, exhibit limited generalization under distribution shifts, and lack explicit memory of past…
Jixin Gao, Fucheng Liu, Teng Zhang, Fusheng Zha
This article presents a kinematic-inertial-LiDAR-visual odometry for humanoid robots, called KILVO. Tailored to the platform features, requirements, and real-world complexity, it fully utilizes the sensors commonly equipped on humanoid robots, including joint encoders, IMU, LiDAR, and camera, within an…
Yuze Fan, Yue Cao, Pengjie Gao, Haojia Gao + 6 more
Frozen vision-language-action (VLA) policies generate temporally extended action chunks, but long-horizon manipulation remains vulnerable to accumulated execution error and visual aliasing across task stages. Successful rollouts provide useful corrective evidence, yet current frame retrieval can return…
Changyuan Wang, Chubin Zhang, Zhenyu Wu, Runhao Li + 7 more
Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. However, their potential remains fundamentally constrained by the scarcity of large-scale embodied trajectory datasets, leading to insufficient…
Zehua Fan, Junjie He, Wenxuan Song, Xi Wang + 12 more
World action models (WAMs) built on video generation backbones are a rising recipe for robot learning, yet remain confined to tabletop manipulation. Mobile manipulation demands simultaneous locomotion and whole-body manipulation amid scene-scale dynamics, yet is still dominated by dynamics-blind visual encoders with…
Jihoon Oh, Kento Kawaharazuka, Kei Okada
Learning manipulation skills from human videos is promising for scalable robot learning. However, the embodiment mismatch between humans and robots makes this challenging. One promising solution is to learn object-centric actionable affordances that are embodiment-agnostic. In this work, we propose a framework that…
Hyoto Yamaguchi, Zenji Yatabe, Seiya Kasai
Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot. To classify ground condition without image sensing and processing, we implemented artificial proprioception by integrating a three-axis accelerometer, eight foot pressure…
Alperen Kenan, Paul Bremner, Manuel Giuliani
Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and…
Taehyeon Kong, Woojin Kim, Jemin Hwangbo
In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions. The proposed estimator directly predicts relative displacement, relative rotation, and body-frame velocity from a…
Kunal S. Narkhede, Abhijeet M. Kulkarni, Guoquan Huang, Ioannis Poulakakis
Autonomous robot navigation requires the rapid generation of obstacle-free regions for trajectory planning. However, existing corridor generators struggle to meet real-time, sensor-rate computational constraints. To resolve this bottleneck, we introduce PathCover, a framework driven by RISP; a novel randomized…
Hanlin Wang, Xin Wang, Xinwei Wei, Jiaxu Liu + 3 more
Acoustic field-driven manipulation provides a non-contact and non-invasive strategy for controlling microscale and nanoscale objects, yet its extension to millimeter-scale robots was limited by insufficient propulsion efficiency in confined biological environments. Here, a coordinated multi-acoustic-field approach is…
Ella Walsh, Spencer Teetaert, Eric Diller, Timothy D. Barfoot + 1 more
Continuum robots often operate in uncertain environments, where accurate state estimation is essential for safe interactions. Estimate uncertainty is inherently spatially non-uniform: confidence varies depending on where measurements are available. Global estimation accuracy is not always the top priority, but rather…