Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an in...
The proposed XRoboToolKit-T teleoperation system incorporates a tactile-informed force control architecture, designed to ensure both stable and precise force control in contact-rich manipulation during teleoperation, which achieves higher data collection efficiency and improved manipulation stability compared to state-...
Xiwen Dengxiong, Xue-Ting Wang, Ke Jing et al.· 0 citations
This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural language descriptions and gesture demonstrations to explain how they want the robots to collectively behave in response to their gestures....
Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis et al.· 0 citations
A wrist-worn pressure-sensing wristband that recovers continuous full-hand pose and distributed contact force on a single wearable is introduced, and the key insight is that muscle contraction and tendon displacement produce pressure patterns, which correlate strongly with hand pose and interaction force.
S. Kolev, Ling-Ni Ma, Michael Goesele et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap between human and robot in both visual appearance and kinematics. We present HumanEgo, a framework that bridges the embodiment gap by lifting...
Time-Adaptive Robotic Control (TARC), a reinforcement learning framework in which the policy jointly predicts a control action and its duration of application, is introduced, and matches the performance of high-frequency discrete-time controllers while operating at less than half their control frequency.
Arnav Sukhija, Lenart Treven, Jin Cheng et al.· arXiv.org· 1 citation
The efficacy of the proposed heterogeneous kernel metrics for Deep Kernel Learning is substantiated through experimental studies on a 1/10th scale racecar platform, demonstrating improved prediction accuracy and thereby safely overtaking against OVs.
Ho-Jin Lee, Youngim Nam, Sang-hoon Lee et al.· IEEE Robotics and Automation...· 1 citation
This work proposes a continual learning framework for traversability prediction that incrementally adapts to new terrains using a generative experience recall model, and incorporates the uncertainty of the generated samples from the recall model, enabling uncertainty-aware adaptation.
Ho-Jin Lee, Yunho Lee, D. Duecker et al.· IEEE Robotics and Automation...· 3 citations
Intrinsic Robot Rewarding (IRR) proposes a reusable approach to learn and improve from the data and experience already available in industrial robot systems, linking reward reliability to task success and supervision effort.
Tobias Schäffer, Mohab Elkhayat, Daniela Nicklas et al.· 0 citations
TEMPO is proposed, which augments a pretrained VLA with two temporal inputs: a motion summary extracted from a frozen video foundation model to resolve motion ambiguity and a compact proprioceptive history to resolve state aliasing.
Zhen-Yang Feng, Jimin Heo, Erik B. Sudderth et al.· 2 citations· ⚡1
The original ACT paper reported that encoder removal dropped the mean success rate from 35% to 2% on two simulated tasks with human demonstrations, but this drop does not reappear in tests, although smaller gains or losses in success rate remain uncertain.
CueNav is presented, a video model-based navigation framework combining visual cue guided video planning with an embodiment-specific Inverse-Dynamics Model (IDM) that paves the way toward a generalizable navigation framework for longer-horizon planning and embodiment-aware control.
Ho-Jin Lee, S. Li, Maximilian Hilger et al.· 0 citations
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
