Standing Self-Manipulation for a Legged Robot

On challenging, uneven terrain a legged robot’s open loop posture will almost inevitably be inefficient, due to uncoordinated support of gravitational loads with coupled internal torques. By reasoning about certain structural properties governing the infinitesimal kinematics of the closed chains arising from a typical stance, we have developed a computationally trivial self-manipulation behavior that can minimize both internal and external torques absent any terrain information. The key to this behavior is a change of basis in torque space that approximates the partially decoupled nature of the two types of disturbances. The new coordinates reveal how to use actuator current measurements as proprioceptive sensors for the approximate gradients of both the internal and external task potential fields, without recourse to further modeling. The behavior is derived using a manipulation framework informed by the dual relationship between a legged robot and a multifingered hand. We implement the reactive posture controller resulting from simple online descent along these proprioceptively sensed gradients on the X-RHex robot to document the significant savings in standing power.

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