Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control

In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization method generates object motions with minimal closed-loop sensitivity to uncertain parameters. The resulting reference trajectory is tracked using a coupled convex model predictive controller that jointly predicts the CoM dynamics of the quadruped and the payload followed by a whole-body QP that enforces ground reaction constraints. The approach is evaluated through extensive simulations and real-world experiments under variations in the object's inertial parameters. Its performance is compared with fixed-orientation and straight-line trajectories as baseline. The results show that the optimized object motion reduces the sliding by approximately 50% compared with the fixed-orientation baseline and 30% compared with the straight-line baseline, while also achieving lower robot CoM tracking errors.

Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, existing teleoperation and data-collection frameworks often overlook the joint consideration of multimodal perception and coordinated whole-body operation. This limitation can reduce the efficiency and quality of demonstration collection, thereby affecting the effectiveness of downstream policy learning. In this work, we present M3-Tele: A Unified Multimodal Teleoperational Framework for Compliant Whole-Body Mobile Manipulation, enabling stable physical interaction and capturing aligned visual, tactile, force, and proprioceptive observations during task execution. Extensive experiments demonstrate that the proposed framework significantly improves contact-rich teleoperation performance. The proposed controller reduces the force tracking error from 4.132N to 0.346N, the contact loss from 2.46 to 0.02 events per trial and the tactile deformation error by 65%. User studies across four mobile manipulation tasks also verify the reliability and usability of the proposed system. Furthermore, Diffusion Policy experiments highlight the value of joint tactile and force sensing.

Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has expanded with recent VR technologies. Many VR teleoperation studies, however, have primarily served as data-collection tools for robot imitation learning, so they often do not explicitly address dynamic obstacles, workspace changes, or collision risks during operation. For real deployment aimed at operator safety, teleoperation must react to dynamic situations with low latency and remain robust to mistakes made by inexperienced operators. This paper presents a VR teleoperation framework that supports real-time manipulation while handling collisions with both static and moving obstacles. The framework integrates GPU-accelerated inverse kinematics and trajectory optimization within a VR interface to generate feasible joint commands at each control cycle under robot constraints. Experiments with a 7-DoF manipulator demonstrate stable online behavior and collision-aware motion generation across three scenarios: obstacle-free, static-obstacle, and moving-obstacle environments. The results indicate that the proposed approach generates motion consistent with the operator's command while producing safe detours when obstacles interfere with the commanded path.