publications

2020

1. Efficient Contact Mode Enumeration in 3D

   Huang, Eric, Cheng, Xianyi, and Mason, Matthew T.

   In Workshop on the Algorithmic Foundations of Robotics, 2020

   Abs

   In a hybrid dynamical system with multiple rigid bodies, the relative motions of the contact points on two colliding bodies may be classified as separating, sticking (moving together), or sliding. Given a physical contact model, the active contact modes determine the dynamic equations of motion. Analogously, the set of all possible (valid) contact mode assignments enumerates the set of all possible dynamical flows of the hybrid dynamical system at a given state. Naturally, queries about the kinematics or dynamics of the system can be framed as computations over the set of possible contact modes. This paper investigates efficient ways to compute this set.
   To that end, we have developed the first efficient 3D contact mode enumeration algorithm. The algorithm is exponential in the degrees of freedom of the system and polynomial in the number of contacts. The exponential term is unavoidable and an example is provided. Prior work in this area has only demonstrated efficient contact mode enumeration in 2D for a single rigid body. We validated our algorithm on peg-in-hole, boxes against walls, and a robot hand grasping an ellipse. Our experimental results indicate real-time contact mode enumeration is possible for small to medium sized systems. Finally, this paper concludes with a discussion of possible related application areas for future work. Ultimately, the goal of this paper is to provide a novel computational tool for researchers to use to simulate, analyze, and control robotic systems that make and break contact with the environment.

2019

1. Large-Scale Multi-Object Rearrangement

   Huang, Eric, Jia, Zhenzhong, and Mason, Matthew T.

   In 2019 International Conference on Robotics and Automation (ICRA) 2019

   Abs

   This paper describes a new robotic tabletop rearrangement system, and presents experimental results. The tasks involve rearranging as many as 30 to 100 blocks, sometimes packed with a density of up to 40%. The high packing factor forces the system to push several objects at a time, making accurate simulation difficult, if not impossible. Nonetheless, the system achieves goals specifying the pose of every object, with an average precision of ± 1 mm and ± 2°. The system searches through policy rollouts of simulated pushing actions, using an Iterated Local Search technique to escape local minima. In real world execution, the system executes just one action from a policy, then uses a vision system to update the estimated task state, and replans. The system accepts a fully general description of task goals, which means it can solve the singulation and separation problems addressed in prior work, but can also solve sorting problems and spell out words, among other things. The paper includes examples of several solved problems, statistical analysis of the system’s behavior on different types of problems, and some discussion of limitations, insights, and future work.

2017

1. Exact Bounds on the Contact Driven Motion of a Sliding Object, With Applications to Robotic Pulling

   Huang, Eric, Bhatia, Ankit, Boots, Byron, and Mason, Matthew T.

   In Proceedings of Robotics: Science and Systems 2017

   Abs

   This paper explores the quasi-static motion of a planar slider being pushed or pulled through a single contact point assumed not to slip. The main contribution is to derive a method for computing exact bounds on the object’s motion for classes of pressure distributions where the center of pressure is known but the distribution of support forces is unknown. The second contribution is to show that the exact motion bounds can be used to plan robotic pulling trajectories that guarantee convergence to the final pose. The planner was tested on the task of pulling an acrylic rectangle to random locations within the robot workspace. The generated plans were accurate to 4.00mm +- 3.02mm of the target position and 4.35 degrees +- 3.14 degrees of the target orientation.

2. Motion planning with graph-based trajectories and Gaussian process inference

   Huang, Eric, Mukadam, Mustafa, Liu, Zhen, and Boots, Byron

   In 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017

   Abs

   Motion planning as trajectory optimization requires generating trajectories that minimize a desired objective function or performance metric. Finding a globally optimal solution is often intractable in practice: despite the existence of fast motion planning algorithms, most are prone to local minima, which may require re-solving the problem multiple times with different initializations. In this work we provide a novel motion planning algorithm, GPMP-GRAPH, that considers a graph-based initialization that simultaneously explores multiple homotopy classes, helping to contend with the local minima problem. Drawing on previous work to represent continuous-time trajectories as samples from a Gaussian process (GP) and formulating the motion planning problem as inference on a factor graph, we construct a graph of interconnected states such that each path through the graph is a valid trajectory and efficient inference can be performed on the collective factor graph. We perform a variety of benchmarks and show that our approach allows the evaluation of an exponential number of trajectories within a fraction of the computational time required to evaluate them one at a time, yielding a more thorough exploration of the solution space and a higher success rate.

2015

1. A General-purpose System for Teleoperation of the DRC-HUBO Humanoid Robot

   Zucker, Matt, Joo, Sungmoon, Grey, Michael X., Rasmussen, Christopher, Huang, Eric, Stilman, Michael, and Bobick, Aaron

   Journal of Field Robotics 2015

   Abs

   We present a general system with a focus on addressing three events of the 2013 DARPA Robotics Challenge (DRC) trials: debris clearing, door opening, and wall breaking. Our hardware platform is DRC-HUBO, a redesigned model of the HUBO2+ humanoid robot developed by KAIST and Rainbow, Inc. Our system allowed a trio of operators to coordinate a 32 degree-of-freedom robot on a variety of complex mobile manipulation tasks using a single, unified approach. In addition to descriptions of the hardware and software, and results as deployed on the DRC-HUBO platform, we present some qualitative analysis of lessons learned from this demanding and difficult challenge.