Kaiyue Xing, Shivansh Pratap Singh, Sarthak Mishra, Di Liu, Spandan Roy, Simone Baldi, Leonid Fridman
原文摘要
This work addresses the challenge of controlling multi-quadrotor systems subject to tight uncertain couplings. We consider interconnection forces and moments among the quadrotors to be state-dependent functions of the coordination errors, without assuming knowledge of their functional/parametric structures, e.g., no linear-in-parameters assumptions are made. We only rely on general state-dependent quadratic bounds valid for several Euler-Lagrange mechanical systems. We propose a coordination strategy embedding adaptive laws dedicated to compensating for the lack of structural knowledge. Unlike conventional decoupled position-attitude designs, we present a coupled stability analysis accounting for the position-attitude couplings induced by the interconnection forces and moments. The analysis, along with real experiments involving cooperative aerial transportation of rope-interconnected payloads, demonstrates the effectiveness of the proposed solution in compensating for these complex interconnections. Comparative experiments show that, when a payload drop induces a disturbance propagating through the ropes across the whole formation, the proposed strategy reduces significantly post-disturbance transients.
Soham Patil, Avirup Das, Sourabh Bhosale, Spandan Roy
原文摘要
Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this embodied perceptual skill, we investigate whether a robot can learn to infer contact from vision, an approach that also offers a scalable alternative to tactile hardware specifically for binary contact estimation, which faces practical challenges in cost, fragility, and integration. We present NoContactNoWorries, a transformer-based multimodal framework that fuses RGB-D vision with the robot's proprioception to infer binary contact states as a pseudo-tactile signal for hand-object interactions. We validate by training a single contact prediction model on multiple objects and show that the inferred contact signal supports downstream reinforcement learning agents for in-hand object reorientation, generalizing to novel objects. Experiments in both simulation and on a real-world robot validate our approach, highlighting the feasibility of inferring contact from vision and proprioception. Project Page: https://soham2560.github.io/no-contact-no-worries/