Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod D.7
Authors: ROY CHOWDHURY, ABHRA; Soh, Gim Song; Foong, Shaohui; Wood, Kristin
Title: Evaluating Robust Trajectory Control of a Miniature Rolling and Spinning Robot in Outdoor Conditions
Abstract:
This paper presents trajectory following control experiments of a miniature spherical rolling and spinning robot mechanism on three different types of outdoor surfaces. The research is inspired from the efficient locomotory rolling patterns of various insects in unstructured environment. A nonlinear adaptive sliding mode (ASMC) feedback method maintains the robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed trajectory following control policy is developed, implemented and tested for the miniature spherical robot on three different types of irregular surfaces in outdoors. Trajectory following accuracy, roll angle stability and wheel velocity response are three parameters measured to evaluate robot performance. (ASMC)controller is compared with an integral sliding (ISMC)controller. Experimental results show that proposed control policy is able to manage an accurate trajectory following amidst robust control of a rolling and spinning robot on three types of irregular surface in practical outdoor conditions.
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod D.7
Authors: ROY CHOWDHURY, ABHRA; Soh, Gim Song; Foong, Shaohui; Wood, Kristin
Title: Evaluating Robust Trajectory Control of a Miniature Rolling and Spinning Robot in Outdoor Conditions
Abstract:
This paper presents trajectory following control experiments of a miniature spherical rolling and spinning robot mechanism on three different types of outdoor surfaces. The research is inspired from the efficient locomotory rolling patterns of various insects in unstructured environment. A nonlinear adaptive sliding mode (ASMC) feedback method maintains the robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed trajectory following control policy is developed, implemented and tested for the miniature spherical robot on three different types of irregular surfaces in outdoors. Trajectory following accuracy, roll angle stability and wheel velocity response are three parameters measured to evaluate robot performance. (ASMC)controller is compared with an integral sliding (ISMC)controller. Experimental results show that proposed control policy is able to manage an accurate trajectory following amidst robust control of a rolling and spinning robot on three types of irregular surface in practical outdoor conditions.










![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)