First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot

Anne, Timothée; Dalin, Eloïse; Bergonzani, Ivan; Ivaldi, Serena; Mouret, Jean-Baptiste

doi:10.1109/lra.2022.3188884

Cited by 2 publications

(1 citation statement)

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“…While convenient for simulation, this method would be very dangerous in actual robot applications. The latest article [22] proposes a D-reflex method, which uses neural networks to learn and predict potential wall brace positions for the robot arm during a fall. The downside is that the robot would need to spend significant time retraining the model if the environment changes.…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

Zuo,

Gao,

Liu

et al. 2024

Biomimetics

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When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms’ optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot’s movement. These constraints include whole-body dynamics, center of mass constraints, arm’s end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%