activity
20132024
most citedMotion Planning for Quadrupedal Locomotion: Coupled Planning, Terrain Mapping and Whole-Body Control

142 citations · 827 across the 40 of their papers we have counts for

collaborators
Showing 2017Show all

10 papers · 1 filter

cs.RO2017★ 1 cited

The Actuation-consistent Wrench Polytope (AWP) and the Feasible Wrench Polytope (FWP)

Romeo Orsolino, Michele Focchi, Carlos Mastalli +3

The motivation of our current research is to devise motion planners for legged locomotion that are able to exploit the robot's actuation capabilities. This means, when possible, to…

cs.RO2017★ 1 cited

Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

João Silvério, Yanlong Huang, Leonel Rozo +2

When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space)…

cs.RO2017★ 61 cited

Application of Wrench based Feasibility Analysis to the Online Trajectory Optimization of Legged Robots

Romeo Orsolino, Michele Focchi, Carlos Mastalli +3

Motion planning in multi-contact scenarios has recently gathered interest within the legged robotics community, however actuator force/torque limits are rarely considered. We belie…

eess.SY2017★ 35 cited

Artificial Delay Based ARC of a Class of Uncertain EL Systems with Only Position Feedback

Spandan Roy, Indra Narayan Kar, Jinoh Lee +2

In this paper, the tracking control problem of an Euler-Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control strategy, christened Ti…

cs.RO2017

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

Anh Nguyen, Dimitrios Kanoulas, Luca Muratore +2

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the inp…

cs.CV2017★ 26 cited

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Thanh-Toan Do, Anh Nguyen, Ian Reid

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an objec…