Heavy lifting tasks via haptic teleoperation of a wheeled humanoid

TL;DR

Dynamic Mobile Manipulation (DMM) on humanoids requires coordinating locomotion, manipulation, and posture under payload-induced disturbances. The authors propose a teleoperation framework that retargets human whole-body motion to the robot and provides explicit haptic feedback, while height variation and payload-aware pitch adjustments compensate for external moments. They compare three control mappings—base-velocity, pitch-based Divergent Component of Motion (DCM) dynamic similarity with automatic compensation, and a manual feedback mode—and validate them with lifts up to $2.5$ kg (approximately 21 percent of $m_R$). The results show automatic lean compensation improves DCM tracking and reduces pilot effort, and a hybrid strategy combining mappings offers best performance across task phases, demonstrating practical potential for DMM in real-world settings.

Abstract

Humanoid robots can support human workers in physically demanding environments by performing tasks that require whole-body coordination, such as lifting and transporting heavy objects.These tasks, which we refer to as Dynamic Mobile Manipulation (DMM), require the simultaneous control of locomotion, manipulation, and posture under dynamic interaction forces. This paper presents a teleoperation framework for DMM on a height-adjustable wheeled humanoid robot for carrying heavy payloads. A Human-Machine Interface (HMI) enables whole-body motion retargeting from the human pilot to the robot by capturing the motion of the human and applying haptic feedback. The pilot uses body motion to regulate robot posture and locomotion, while arm movements guide manipulation.Real time haptic feedback delivers end effector wrenches and balance related cues, closing the loop between human perception and robot environment interaction. We evaluate the different telelocomotion mappings that offer varying levels of balance assistance, allowing the pilot to either manually or automatically regulate the robot's lean in response to payload-induced disturbances. The system is validated in experiments involving dynamic lifting of barbells and boxes up to 2.5 kg (21% of robot mass), demonstrating coordinated whole-body control, height variation, and disturbance handling under pilot guidance. Video demo can be found at: https://youtu.be/jF270_bG1h8?feature=shared

Figures (5)

• Figure 1: A human operator controls the robot to use its whole body to lift a barbell 21% of its weight. The robot automatically leans backward to compensate for the additional weight, allowing the human to remain mostly upright.
• Figure 2: Left: The haptic force feedback is generated to produce a moment around the ankle of the human pendular model. Right: The robot reduced model computes a new desired pitch angle to counteract the external moment from the object mass.
• Figure 3: (A) highlights control layout for experiments where the robot automatically compensates for the external moment by estimating a new desired pitch. (B) shows the layout where the moment is fed back entirely to the pilot through dynamic similarity retargeting.
• Figure 4: Left: A pilot operates the robot to pick and place a 2.5 kg (21 % of $m_R$) box at different heights. Right: Plots comparing the DCM pitch-based mapping (blue) with the baseline velocity mapping (red). The DCM mapping with force feedback results in better DCM tracking. Note that in this experiment, the robot grasps the box at an elevated height and places it at a lower height using the DCM pitch mapping, while the opposite is performed using the velocity mapping. Similar results are obtained for the reverse order.
• Figure 5: Without setpoint estimation, the pilot must resist nearly 200 N of haptic force to balance the robot holding a heavy object.