Loco-manipulation planning skills are pivotal for expanding the utility ...
Adaptive falling and recovery skills greatly extend the applicability of...
Mobile manipulation in robotics is challenging due to the need of solvin...
Dynamic locomotion in rough terrain requires accurate foot placement,
co...
Model Predictive Control (MPC) schemes have proven their efficiency in
c...
Terrain geometry is, in general, non-smooth, non-linear, non-convex, and...
The ability to generate dynamic walking in real-time for bipedal robots ...
This paper introduces a novel approach for whole-body motion planning an...
In this paper, we present a real-time whole-body planner for collision-f...
Deep reinforcement learning produces robust locomotion policies for legg...
Modern robotic systems are endowed with superior mobility and mechanical...
Modern, torque-controlled service robots can regulate contact forces whe...
Planning for legged-wheeled machines is typically done using trajectory
...
We present a learning algorithm for training a single policy that imitat...
We present a model predictive controller (MPC) that automatically discov...
A kitchen assistant needs to operate human-scale objects, such as cabine...
In this work, we present a learning-based pipeline to realise local
navi...
In this paper, we propose a whole-body planning framework that unifies
d...
The Sequential Linear Quadratic (SLQ) algorithm is a continuous-time var...
Quadrupedal robots are skillful at locomotion tasks while lacking
manipu...
We present a fully-integrated sensing and control system which enables m...
In this paper, we introduce an actor-critic algorithm called Deep Value ...
This paper addresses the problem of legged locomotion in non-flat terrai...
We present an Imitation Learning approach for the control of dynamical
s...
The computational power of mobile robots is currently insufficient to ac...
Locomotion planning for legged systems requires reasoning about suitable...
Autonomous mobile manipulation is the cutting edge of the modern robotic...
Transferring solutions found by trajectory optimization to robotic hardw...
We introduce a real-time, constrained, nonlinear Model Predictive Contro...