Christopher M. Dellin

CHOMP: Covariant Hamiltonian optimization for motion planning

In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot.

CHIMP, the CMU Highly Intelligent Mobile Platform

We have developed the CHIMP CMU Highly Intelligent Mobile Platform robot as a platform for executing complex tasks in dangerous, degraded, human-engineered environments. CHIMP has a near-human form factor, work-envelope, strength, and dexterity to work effectively in these environments. It avoids the need for complex control by maintaining static rather than dynamic stability. Utilizing various sensors embedded in the robots head, CHIMP generates full three-dimensional representations of its environment and transmits these models to a human operator to achieve latency-free situational awareness. This awareness is used to visualize the robot within its environment and preview candidate free-space motions. Operators using CHIMP are able to select between task, workspace, and joint space control modes to trade between speed and generality. Thus, they are able to perform remote tasks quickly, confidently, and reliably, due to the overall design of the robot and software. CHIMPs hardware was designed, built, and tested over 15i¾?months leading up to the DARPA Robotics Challenge. The software was developed in parallel using surrogate hardware and simulation tools. Over a six-week span prior to the DRC Trials, the software was ported to the robot, the system was debugged, and the tasks were practiced continuously. Given the aggressive schedule leading to the DRC Trials, development of CHIMP focused primarily on manipulation tasks. Nonetheless, our team finished 3rd out of 16. With an upcoming year to develop new software for CHIMP, we look forward to improving the robots capability and increasing its speed to compete in the DRC Finals.

Guided Manipulation Planning at the DARPA Robotics Challenge Trials

We document the empirical results from Carnegie Mellon University’s entry into the DARPA Robotics Challenge Trials. Our system seamlessly and intelligently integrates recent advances in autonomous manipulation with the perspective and intuition of an expert human operator. Virtual fixtures are used as the common language between the operator and the motion planner. The planning system then solves a guided manipulation problem to perform disaster-response tasks.

Pregrasp Manipulation as Trajectory Optimization.

We explore the combined planning of pregrasp manipulation and transport tasks. We formulate this problem as a simultaneous optimization of pregrasp and transport trajectories to minimize overall cost. Next, we reduce this simultaneous optimization problem to an optimization of the transport trajectory with start-point costs and demonstrate how to use physically realistic planners to compute the cost of bringing the object to these start-points. We show how to solve this optimization problem by extending functional gradient-descent methods and demonstrate our planner on two bimanual manipulation platforms.

A framework for extreme locomotion planning

A person practicing parkour is an incredible display of intelligent planning; he must reason carefully about his velocity and contact placement far into the future in order to locomote quickly through an environment. We seek to develop planners that will enable robotic systems to replicate this performance. An ideal planner can learn from examples and formulate feasible full-body plans to traverse a new environment. The proposed approach uses momentum equivalence to reduce the full-body system into a simplified one. Low-dimensional trajectory primitives are then composed by a sampling planner called Sampled Composition A* to produce candidate solutions that are adjusted by a trajectory optimizer and mapped to a full-body robot. Using primitives collected from a variety of sources, this technique is able to produce solutions to an assortment of simulated locomotion problems.

Developing a Robust Disaster Response Robot: CHIMPźand the Robotics Challenge

CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex tasks in dangerous, degraded, human-engineered environments, such as those found in disaster response scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot performs manipulation tasks using an upright posture, yet it uses more stable prostrate postures for mobility through difficult terrain. In this paper, we report on the improvements made to CHIMP-both in its mechanical design and its software systems-in preparation for the DARPA Robotics Challenge Finals in June 2015. These include details on CHIMPs novel mechanical design, actuation systems, robust construction, all-terrain mobility, supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide an overview of CHIMPs performance, and we detail the various lessons learned over the course of the challenge. CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing in 3rd place out of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a testament to the robustness engineered into the robot and a remote operators ability to execute complex tasks using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster response robot.

A general technique for fast comprehensive multi-root planning on graphs by coloring vertices and deferring edges

We formulate and study the comprehensive multi-root (CMR) planning problem, in which feasible paths are desired between multiple regions. We propose two primary contributions which allow us to extend state-of-the-art sampling-based planners. First, we propose the notion of vertex coloring as a compact representation of the CMR objective on graphs. Second, we propose a method for deferring edge evaluations which do not advance our objective, by way of a simple criterion over these vertex colorings. The resulting approach can be applied to any CMR-agnostic graph-based planner which evaluates a sequence of edges. We prove that the theoretical performance of the colored algorithm is always strictly better than (or equal to) that of the corresponding uncolored version. We then apply the approach to the Probabalistic RoadMap (PRM) algorithm; the resulting Colored Probabalistic RoadMap (cPRM) is illustrated on 2D and 7D CMR problems.

A System for Multi-step Mobile Manipulation: Architecture, Algorithms, and Experiments

Household manipulation presents a challenge to robots because it requires perceiving a variety of objects, planning multi-step motions, and recovering from failure. This paper presents practical techniques that improve performance in these areas by considering the complete system in the context of this specific domain. We validate these techniques on a table-clearing task that involves loading objects into a tray and transporting it. The results show that these techniques improve success rate and task completion time by incorporating expected real-world performance into the system design.

Pareto-optimal search over configuration space beliefs for anytime motion planning

We present POMP (Pareto Optimal Motion Planner), an anytime algorithm for geometric path planning on roadmaps. For robots with several degrees of freedom, collision checks are computationally expensive and often dominate planning time. Our goal is to minimize the number of collision checks for obtaining the first feasible path and successively shorter feasible paths. We assume that the roadmaps we search over are embedded in a continuous ambient space, where nearby points tend to share the same collision state. This enables us to formulate a probabilistic model that computes the probability of unevaluated configurations being collision-free. We update the model over time as more checks are performed. This model lets us define a weighting function for roadmap edges that is related to the probability of the edge being in collision. Our approach is to trade off between these two weights, gradually prioritizing edge length over collision likelihood. We also show that this tradeoff is approximately equivalent to minimizing the expected path length, with a penalty of being in collision. Our experiments demonstrate that POMP performs comparably with RRTConnect and LazyPRM for the first feasible path, and BIT* for anytime performance, both in terms of collision checks and total planning time.