Avik De
University of Pennsylvania
28 Papers
74 Citations
Avik De is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Computer science & Hybrid system. The author has an hindex of 11, co-authored 26 publications. Previous affiliations of Avik De include Harvard University & Johns Hopkins University.
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Papers
Design Principles for a Family of Direct-Drive Legged Robots
Gavin D. Kenneally,Avik De,Daniel E. Koditschek +2 more
- 11 Feb 2016
TL;DR: This letter introduces Minitaur, a dynamically running and leaping quadruped, which represents a novel class of direct-drive (DD) legged robots, affording highly energetic behaviors across this family of machines despite severe limitations in specific force.
Parallel composition of templates for tail-energized planar hopping
Avik De,Daniel E. Koditschek +1 more
- 26 May 2015
TL;DR: A 4DOF tailed monoped that hops along a boom permitting free sagittal plane motion and a partial proof of correctness for this parallel composition of “template” reference systems suggesting these templates are “anchored” as evidenced by the correspondence of their characteristic motions with a suitably transformed image of traces from the physical platform.
Vertical hopper compositions for preflexive and feedback-stabilized quadrupedal bounding, pacing, pronking, and trotting:
Avik De,Daniel E. Koditschek +1 more
TL;DR: This paper applies an extension of classical averaging methods to hybrid dynamical systems, thereby achieving formally specified, physically effective and robust instances of all virtual bipedal gaits on a quadrupedal robot.
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The Penn Jerboa: A Platform for Exploring Parallel Composition of Templates
Avik De,Daniel E. Koditschek +1 more
TL;DR: This work reports on the development of one important example of such a behavioral programming method, the construction of a novel monopedal sagittal plane hopping gait through parallel composition of four decoupled 1DOF base controllers.
Haptic feedback enhances rhythmic motor control by reducing variability, not improving convergence rate
TL;DR: It is shown that a force impulse to the hand at the moment of ball-paddle collision categorically improves performance over visual feedback alone, not by regulating the rate of convergence to steady state, but rather by reducing cycle-to-cycle variability.