Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod D.5
Authors: Luo, Yudong; Zhao, Na; Kim, Kwang; Yi, Jingang; Shen, Yantao
Title: Inchworm Locomotion Mechanism Inspired Self-Deformable Capsule-Like Robot: Design, Modeling, and Experimental Validation
Abstract:
Inspired by the inchworm locomotion mechanism, this paper presents our recently developed self-deformable capsule-like robot. The robot has the actuated deformation capability that relies on a novel rigid elements-based morphing structure (REMS) and its soft actuation mechanisms. When the robot deforms, it generates the crawling locomotion behavior and thus friction waves between the robot and contact surface to facilitate the inchworm-like crawling movement. The paper starts reviewing the deformable properties of natural biological entities like capsules, presents state of the art of the current capsule-like robots, and details the bio-inspired design of the self-deformable capsule-like robot by describing the model of robot kinematics and its locomotion mechanism. Both simulation and experimental results validate the excellent performance of this capsule-like robot. The developed self-deformable capsule-like robot has the advantage of crawling on varied surfaces and it also has the capabilities to crawl in a variety of narrow pipes based on the deformation elicited locomotion nature of the robot.
ICRA 2018 Spotlight Video
Interactive Session Thu PM Pod D.5
Authors: Luo, Yudong; Zhao, Na; Kim, Kwang; Yi, Jingang; Shen, Yantao
Title: Inchworm Locomotion Mechanism Inspired Self-Deformable Capsule-Like Robot: Design, Modeling, and Experimental Validation
Abstract:
Inspired by the inchworm locomotion mechanism, this paper presents our recently developed self-deformable capsule-like robot. The robot has the actuated deformation capability that relies on a novel rigid elements-based morphing structure (REMS) and its soft actuation mechanisms. When the robot deforms, it generates the crawling locomotion behavior and thus friction waves between the robot and contact surface to facilitate the inchworm-like crawling movement. The paper starts reviewing the deformable properties of natural biological entities like capsules, presents state of the art of the current capsule-like robots, and details the bio-inspired design of the self-deformable capsule-like robot by describing the model of robot kinematics and its locomotion mechanism. Both simulation and experimental results validate the excellent performance of this capsule-like robot. The developed self-deformable capsule-like robot has the advantage of crawling on varied surfaces and it also has the capabilities to crawl in a variety of narrow pipes based on the deformation elicited locomotion nature of the robot.
![Learning to Parse Natural Language to Grounded Reward Functions with Weak Supervision
ICRA 2018 Spotlight Video
Interactive Session Wed PM Pod I.1
Authors: Williams, Edward; Gopalan, Nakul; Rhee, Mina; Tellex, Stefanie
Title: Learning to Parse Natural Language to Grounded Reward Functions with Weak Supervision
Abstract:
In order to intuitively and efficiently collaborate with humans, robots must learn to complete tasks specified using natural language. We represent natural language instructions as goal-state reward functions specified using lambda calculus. Using reward functions as language representations allows robots to plan efficiently in stochastic environments. To map sentences to such reward functions, we learn a weighted linear Combinatory Categorial Grammar (CCG) semantic parser. The parser, including both parameters and the CCG lexicon, is learned from a validation procedure that does not require execution of a planner, annotating reward functions, or labeling parse trees, unlike prior approaches. To learn a CCG lexicon and parse weights, we use coarse lexical generation and validation-driven perceptron weight updates using the approach of Artzi and Zettlemoyer [4]. We present results on the Cleanup World domain [19] to demonstrate the potential of our approach. We report an F1 score of 0.82 on a collected corpus of 23 tasks containing combinations of nested referential expressions, comparators and object properties with 2037 corresponding sentences. Our goal-condition learning approach enables an improvement of orders of magnitude in computation time over a baseline that performs planning during learning, while achieving comparable results. Further, we conduct an experiment with just 6 labeled demonstrations to show the ease of teaching a robot behaviors using our method. Learning to Parse Natural Language to Grounded Reward Functions with Weak Supervision](https://i.ytimg.com/vi/c9Up1R_jlew/mqdefault.jpg)









