Wave-Based Bilateral Teleoperation between Nonlinear Manipulators with Direct Contact Force Feedback

Existing bilateral teleoperation platforms still rely on costly rigid six-axis force/torque sensors, tightly coupled leader-follower hardware, and kilohertz control loops. We present a Cartesian bilateral framework built on the hardware-agnostic WinGs Operating Studio (WOS) middleware, in which a low-cost compliant 6-DOF pose-and-force sensing end-effector, Delta6, is mounted on both sides so that each manipulator behaves as an end-effector 6-DOF series elastic actuator (SEA). The leader runs a damping-only admittance loop with a 6-D biquad notch filter; the follower realizes a stiffness-damping impedance through a position-based outer loop with a PID wrench-to-pose mapping. Three time scales (hardware I/O, mid-rate impedance/admittance, low-rate teleoperation messages) are explicitly decoupled, enabling the same application to drive heterogeneous arms. On a Lite6/FR3 testbed at 150 Hz, the system tracks stably under delays up to $120\pm40$ ms and 1% packet loss, matches the prescribed virtual stiffness in contact, and shows a favorable cumulative energy signature in passivity-style tests.

Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this process. However, manufacturer-provided disturbance-torque estimates on commercial robotic arms are often inaccurate, while the use of end-effector force/torque sensors adds cost and payload. We propose LETS, a physics-guided learning approach that assigns rigid-body dynamics to recursive Newton-Euler inverse dynamics and reserves learning for only the robot-specific residual. LETS uses only the robot's URDF model and joint feedback, deliberately excluding control commands and thereby decoupling external-torque estimation from control-command inputs. It trains on 12 minutes of free-motion data in under one minute, without requiring a dedicated end-effector six-axis force/torque sensor. In bilateral teleoperation, LETS achieves real-time external-torque estimation that enables stable force feedback. Its free-space error is 69% lower than the manufacturer-provided estimate, substantially reducing coupled oscillations between leader and follower; its contact error is 28% lower, yielding more accurate haptic feedback.

This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enhanced operator embodiment through virtual reality (VR) and distributed haptic feedback. The platform integrates a full-scale industrial hydraulic manipulator, a 7-DoF haptic exoskeleton, and head-tracked visual feedback to reinforce operator agency and self-location. A force-sensorless adaptive controller incorporates an augmented human-robot dynamic model to address master-surrogate asymmetries, input nonlinearities, model uncertainties, and communication delays. Rigorous analysis establishes semi-global uniform ultimate boundedness of the closed-loop system. Extensive full-scale experiments demonstrate precise motion and force tracking with scaling up to 1:13 and 1:1000, respectively, and fixed or time-varying delays up to 150 ms. Coordinated 6-DoF pick-and-place and multi-axis contact tasks further validate system performance. A ten-participant user study supports system usability and indicates approximately 50% higher normalized embodiment scores with VR than with monitor-based feedback. These results advance intuitive, force-reflected remote manipulation at industrial scale.