Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human-Robot Collaboration

• URL: http://arxiv.org/abs/2603.08089v1
• Date: Mon, 09 Mar 2026 08:30:14 GMT
• Title: Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human-Robot Collaboration
• Abstract summary: Human-robot collaboration aims to extend human ability through cooperation with robots.<n>Being able to enhance the performance of both sides, such that human-robot collaboration outperforms a single robot/human, remains an open issue.<n>New control scheme has been proposed for redundant robots, consisting of an adaptive vision-based control term in task space and an interactive control term in null space.
• Score: 7.779638839381672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-robot collaboration aims to extend human ability through cooperation with robots. This technology is currently helping people with physical disabilities, has transformed the manufacturing process of companies, improved surgical performance, and will likely revolutionize the daily lives of everyone in the future. Being able to enhance the performance of both sides, such that human-robot collaboration outperforms a single robot/human, remains an open issue. For safer and more effective collaboration, a new control scheme has been proposed for redundant robots in this paper, consisting of an adaptive vision-based control term in task space and an interactive control term in null space. Such a formulation allows the robot to autonomously carry out tasks in an unknown environment without prior calibration while also interacting with humans to deal with unforeseen changes (e.g., potential collision, temporary needs) under the redundant configuration. The decoupling between task space and null space helps to explore the collaboration safely and effectively without affecting the main task of the robot end-effector. The stability of the closed-loop system has been rigorously proved with Lyapunov methods, and both the convergence of the position error in task space and that of the damping model in null space are guaranteed. The experimental results of a robot manipulator guided with the technology of augmented reality (AR) are presented to illustrate the performance of the control scheme.

Related papers

• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration [67.13034606664333]
  EgoHumanoid is the first framework to co-train a vision-language-action policy using egocentric human demonstrations.<n>A portable system for scalable human data collection is developed.
  arXiv  Detail & Related papers  (2026-02-10T18:59:03Z)
• Fault-Tolerant Multi-Robot Coordination with Limited Sensing within Confined Environments [0.6144680854063939]
  We propose a novel fault-tolerance technique leveraging physical contact interactions in multi-robot systems.<n>We introduce the "Active Contact Response" (ACR) method, where each robot modulates its behavior based on the likelihood of encountering an inoperative (faulty) robot.
  arXiv  Detail & Related papers  (2025-05-21T02:43:36Z)
• HACTS: a Human-As-Copilot Teleoperation System for Robot Learning [47.9126187195398]
  We introduce HACTS (Human-As-Copilot Teleoperation System), a novel system that establishes bilateral, real-time joint synchronization between a robot arm and teleoperation hardware.<n>This simple yet effective feedback mechanism, akin to a steering wheel in autonomous vehicles, enables the human copilot to intervene seamlessly while collecting action-correction data for future learning.
  arXiv  Detail & Related papers  (2025-03-31T13:28:13Z)
• Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
  We introduce a novel system for joint learning between human operators and robots. It enables human operators to share control of a robot end-effector with a learned assistive agent. It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
  arXiv  Detail & Related papers  (2024-06-29T03:37:29Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)
• Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control [17.270360447188196]
  We present a system for human-robot collaborative assembly using learning from demonstration and pose estimation. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario.
  arXiv  Detail & Related papers  (2022-12-02T20:35:55Z)
• SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments [1.5229257192293197]
  We propose a novel methodology for reactive whole-body obstacle avoidance. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.
  arXiv  Detail & Related papers  (2022-03-24T21:11:43Z)
• Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together [68.44697646919515]
  This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
  arXiv  Detail & Related papers  (2022-02-03T10:04:26Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.