Cross-pose estimation between rigid objects is a fundamental building block for robotic applications. In this paper, we propose a new cross-pose estimation method that predicts correspondences on a set level as opposed to a point level. This contrasts methods that predict cross-pose from per-point correspondences, which can encounter optimization problems for objects with symmetries, since each point may have multiple valid correspondences. Our method, SCAlign, consists of a Set Correspondence Network (SCN) which predicts these sets and their correspondences, and an alignment module to compute their relative cross-pose. Taking point clouds of two objects as input, SCN predicts a set label for each point such that such that points that share a set label form a cross object correspondence. The alignment module then computes the cross-pose as the SE(3) transformation that aligns these set correspondences. We compare SCAlign against other cross-pose estimation baselines on a synthetically generated dataset, SynWidth, which contains randomly generated width-mate objects with symmetric or near-symmetric intercepts. SCAlign significantly outperforms the baselines on this challenging dataset. Additionally, we show that set correspondences can be leveraged to distinguish positive and negative matches between pegs and holes. Robot experiments further validate the practical application of this approach.

Inspired by the ability of animals to rely on proprioception and vestibular feedback to adapt their gait, we propose a modular framework for autonomous locomotion that relies on force sensing and inertial information. A first controller exploits anti-compliance, a new application of positive force feedback, to quickly react against obstacles upon impact. We hypothesize that, in situations where a robot experiences occasional impacts with the environment, anti-compliance can help negotiate unknown obstacles, similar to biological systems where positive feedback enables fast responses to external stimuli. A novel parallel controller, based on a bi-stable dynamical system, continuously adjusts the robot's direction of locomotion, and reverts it in reaction to major swerves. We present experimental results, demonstrating how our framework allows a snake robot to autonomously locomote through a row of unevenly-spaced obstacles. Finally, we extend our proprioceptive controller to legged locomotion, showing how a hexaprint robot can adapt its motion to climb over obstacles.