News

Learn more of our recent work through the mini workshop.

0:00 Tianhao Wei: Robotic safety with Neural Network Components

11:50 Ruixuan Liu: Towards safe human-robot collaboration

25:40 Rui Chen and Alvin Shek: Real-time Collaborative Robot Handling with Recurrent Encoder-Decoder Attentive Neural Process

39:06 Letian Wang: Hierarchical, Adaptable and Transferable Networks (HATN) forDriving Behavior Generation

Ruixuan finishes his master study on Data-Efficient Behavior Prediction for Safe Human-Robot Collaboration. His thesis can be found here.

The organizers reported the results of VNN-COMP’21 on 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS) affiliated with CAV 2021. The benchmarks used in the competition can be found here. The final slides can be found here. The final report will be out soon.

Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

Feasible Region-based Identification Using Duality

Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control

Congratulations Jaskaran and Weiye!

Human Motion Prediction Using Adaptable Recurrent Neural Networks and Inverse Kinematics

Chase Noren, 2nd year PhD student in the lab, gets the prestigious Uber PhD Fellowship.

Tolerance-guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion

Contact-Rich Trajectory Generation in Confined Environment using Iterative Convex Optimization

Multi-car Convex Feasible Set Algorithm in Trajectory Planning

Weiye’s paper Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization gets the Best Student Paper Finalist in DSCC 2020.

Experimental Evaluation of Human Motion Prediction Toward Safe and Efficient Human Robot Collaboration

Robust online model adaptation by EKF with exponential moving average and dynamic multi-epoch strategy (L4DC)

Why does symmetry cause deadlocks? (IFAC World Congress)

The project “Hierarchical Motion Planning for Efficient and Provably Safe Human-Robot Interactions” received 2019 Amazon Research Reward.

Tianhao presented the paper Safe control algorithms using energy functions: A unified framework, benchmark, and new directions in the 58th IEEE Conference on Decision and Control. The code is available here.

Siyan presented the paper Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration in the AAAI Fall Symposium Series - AI for HRI. This work was done by Abulikemu and Siyan during their internships in ICL. More information can be found in the poster and the presentation slides.

The book “Designing Robot Behavior in Human-Robot Interactions” is available here.

Jaskaran Grover, 2nd year PhD student in the lab, gets the prestigious Uber Presidential Fellowship.

The Robotiq Blog posted an interview with Changliu (link). In this interview, Changliu talks cobot safety, the importance of having realistic expectations of what cobots can and can’t do and her vision of a manufacturing world after cobots.

We will organize a workshop at Stanford on June 7 and June 10 introducing the toolbox NeuralVerification.jl. Agenda can be found here.

We will present our paper “AGen - Adaptable generative prediction networks for autonomous driving” in IV2019 in June. Preprints will be available soon. This work is supported by Holomatic. We would like to thank our sponsor for their generous support.

A group of high school students and teachers from Choate visited the Intelligent Control Lab.

Our paper “NeuralVerification.jl: Algorithms for Verifying Deep Neural Networks” has won the Best Applied Paper award at the ICLR 2019 Workshop on Debugging Machine Learning Models. This paper will be presented at the workshop on May 6.

FANUC generously donated an industrial robot arm LR Mate 200iD/7L to the Intelligent Control Lab to support our research. We look forward to developing more pratical methodologies and industrial applications with the robot arm.

This paper surveys emerging algorithms to verify whether a deep neural network satisfies certain input-output properties. We will present the work at AAAI 2019 Spring Symposium Verification of Neural Networks (VNN19) on March 25, and ICLR 2019 Workshop on Debugging Machine Learning Models (DebugML-19) on May 6.