This page hosts selected videos highlighting excutions of robot tasks, as planned by our algorithms. For a more comprehensive set of videos, please check our YouTube channel.
Declutter with Deep Interaction Prediction Network (DIPN) ● Recycling Automation Highlight ● DDM ● Efficient High Quality Stack Rearrangement ● Tabletop Object Rearrangement with Overhand Grasps ● micro-Multi-Vehicle Platform ● Multi-Robot Path Planning ● Optimal Formation Reconfiguration ● Rendezvous Without Coordinates
We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex
interactions that ensue as a robot end-effector pushes multiple objects, whose physical
properties, including size, shape, mass, and friction coefficients may be unknown a priori.
DIPN "imagines" the effect of a push action and generates an accurate synthetic image of
the predicted outcome. DIPN is shown to be sample efficient when trained in simulation
or with a real robotic system. The high accuracy of DIPN allows direct integration with
a grasp network, yielding a robotic manipulation system capable of executing challenging
clutter removal tasks while being trained in a fully self-supervised manner. The overall
network demonstrates intelligent behavior in selecting proper actions between push and
grasp for completing clutter removal tasks and significantly outperforms the previous
state-of-the-art. Remarkably, DIPN achieves even better performance on the real robotic
hardware system than in simulation.
Computer Vision and Robotic System for Recycling Automation
- We are working with a large recycling company to help it modernizing its
product lines to increase levels of automation as an early prototype, we build a demo
system for the separation of scrap metals based on color that is robust to lighting
condition changes. The video shows a real-time demo of a run that separates pure copper
pieces from mixed aluminum-copper pieces. We expect to report more exciting results
as the project progresses.
- DDM solves one-shot and dynamic optimal multi-robot path
planning problems in a graph-based setting. DDM
is mainly enabled through exploiting two innovative heuristics: path
diversification and optimal sub-problem solution databases. The two
heuristics attack two distinct phases of a decoupling-based planning
planner: while path diversification allows more effective use of the
entire workspace for robot travel, optimal sub-problem solution databases
facilitate the fast resolution of local path conflicts.
Efficient High Quality Stack Rearrangement
- Video highlight of our RA-L/ICRA 2018 work with the same name.
Abstract: We study a variant of rearrangement problems that appear frequently
in applications, which involves sorting objects or robots in stack-like
containers that can be accessed only from one side. We provide efficient
algorithms that could generate high quality rearrangement sequence.
Optimal Tabletop Object Rearrangement with Overhand Grasps
- Video highlight of our RSS 2017 work on optimal tabletop object
rearrangement and subsequent extended version. Our hardware experiments
confirm our hypothesis that (1) grasping/releasing is generally much more
time consuming and (2) our proposed algorithm provide significant benefit
when compared with a greedy algorithm.
A Portable, 3D-Printing Enabled Multi-Vehicle Platform for Robotics Research and Education
- Video highlight of our microMVP platform for all! See https://arc.cs.rutgers.edu/mvp/
for more details or read more here.
Near-Optimal Multi-Robot Path Planning in Continuous Domain - Video highlights accompanying
our ISRR work. You may also [download the video].
Optimal Reconfiguration of Multi-Robot Formations - In two
(CDC'12, CDC'13) recent works,
we developed efficient algorithm for the distance optimal reconfiguration of multi-robot formations. The video below demonstrates
effectiveness of the algorithm. We note that the examples in the video take less than 0.1 second to solve when
implemented in Java and running on a MacBook Air (2013).
[download the video].
Rendezvous Without Coordinates - This research establishes a sufficient condition for an arbitrary
(known) number of Dubins-car vehicles to rendezvous in finite time. The sensing model of the vehicle is extremely coarse
with only three quantized values. The feedback control law is similarly quantized with three total control input. In particular,
the vehicles do not perform any state estimation, i.e., no coordinate data is needed. Our result generalizes to distributed
systems without central coordination as well as in-homogeneous vehicles.
The video below demonstrates the sufficient condition for rendezvous, which depends solely on the sensor quantization (windshield size). We show two cases of rendezvous and two cases of divergence. Time evolutions of both the system and the Lyapunov certificate are shown. The simulation program is fully accessible here. [download the video].