Team RoboSimian: Semi‐autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

Karumanchi, Sisir; Edelberg, Kyle; Baldwin, Ian; Nash, Jeremy; Reid, John E.; Bergh, Charles F.; Leichty, John; Carpenter, Kalind; Shekels, Matthew; Gildner, Matthew; Newill-Smith, David; Carlton, Jason; Koehler, John; Dobreva, Tatyana; Frost, Matthew; Hebert, Paul D. N.; Borders, James; Ma, Jie; Douillard, Bertrand; Backes, Paul; Kennedy, Brett; Satzinger, Brian; Lau, Chelsea; Byl, Katie; Shankar, Krishna; Burdick, Joel W.

doi:10.1002/rob.21676

Cited by 68 publications

(28 citation statements)

References 39 publications

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“…The next task we consider is locomoting the system from the origin (0,0) to a goal location (x g , y g ), in particular (5,0)[m], over randomly varying, sinusoidally-smooth terrain with varying friction coefficients. For this task, the observation spaces from the maximum velocity skating task are augmented with the global goal coordinates, negative distance to the goal from the robot's current location, and the angle between the robot's heading and the goal, as discussed in the latter part of Section IV-A.…”

Section: B Skate To Goal Under Uncertaintymentioning

confidence: 99%

“…Without loss of generality to other systems with end effectors, this work aims specifically at increasing robustness and stability of skating motions designed for JPL's Robosimian quadruped [1], [2], [3], [4], [5], which is shown in Figure 1. Previous work in [6] describes an overview of hand-designed skating motions on passive unactuated wheels mounted at each forearm of Robosimian's four identical limbs, comparing specifically skating with three vs. four wheels in contact with the ground.…”

Section: Introductionmentioning

confidence: 99%

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Training in Task Space to Speed Up and Guide Reinforcement Learning

Bellegarda

Byl

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems. However, even with the current state-of-the-art algorithms and computational resources, these algorithms are still plagued with high sample complexity, and thus long training times, especially for high degree of freedom (DOF) systems. There are also concerns arising from lack of perceived stability or robustness guarantees from emerging policies. This paper aims at mitigating these drawbacks by: (1) modeling a complex, high DOF system with a representative simple one, (2) making explicit use of forward and inverse kinematics without forcing the RL algorithm to "learn" them on its own, and (3) learning locomotion policies in Cartesian space instead of joint space. In this paper these methods are applied to JPL's Robosimian, but can be readily used on any system with a base and end effector(s). These locomotion policies can be produced in just a few minutes, trained on a single laptop. We compare the robustness of the resulting learned policies to those of other control methods. An accompanying video for this paper can be found at https://youtu.be/xDxxSw5ahnc.

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Section: B Skate To Goal Under Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Training in Task Space to Speed Up and Guide Reinforcement Learning

Bellegarda

Byl

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Most of the existing approaches have achieved this goal by relying on teleoperation . Supervisory steering and gas commands are sent to the robot to drive the car in Karumanchi et al propose a hybrid solution, with teleoperated steering and autonomous speed control.…”

Section: Problem Formulation and Proposed Approachmentioning

confidence: 99%

“…Most of the existing approaches have achieved this goal by relying on teleoperation. [16][17][18][19][20][21][22] Supervisory steering and gas commands are sent to the robot to drive the car in Karumanchi et al 23 ; DeDonato and colleagues 24 propose a hybrid solution, with teleoperated steering and autonomous speed control. The velocity of the car, estimated with stereo cameras, is fed back to a proportional integral (PI) controller, whereas light imaging, detection, and ranging (LIDAR), IMU, and visual odometry data support the operator during the steering procedures.…”

Section: Problem Formulation and Proposed Approachmentioning

confidence: 99%

Autonomous car driving by a humanoid robot

Paolillo

Gergondet

Cherubini

et al. 2017

Journal of Field Robotics

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Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor‐based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP‐2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.

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“…Univ. of Munich [21], [22], CHIMP of CMU [23], Robosimian of NASA JPL [24], and more. These actuators have precise position control and high torque density.…”

Section: Introductionmentioning

confidence: 99%

Investigations of a Robotic Test Bed With Viscoelastic Liquid Cooled Actuators

Kim

Ahn

Campbell

et al. 2018

IEEE/ASME Trans. Mechatron.

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We design, build, and thoroughly test a new type of actuator dubbed viscoelastic liquid cooled actuator (VLCA) for robotic applications. VLCAs excel in the following five critical axes of performance: energy efficiency, torque density, impact resistence, joint position and force controllability. We first study the design objectives and choices of the VLCA to enhance the performance on the needed criteria. We follow by an investigation on viscoelastic materials in terms of their damping, viscous and hysteresis properties as well as parameters related to the longterm performance. As part of the actuator design, we configure a disturbance observer to provide high-fidelity force control to enable a wide range of impedance control capabilities. We proceed to design a robotic system capable to lift payloads of 32.5 kg, which is three times larger than its own weight. In addition, we experiment with Cartesian trajectory control up to 2 Hz with a vertical range of motion of 32 cm while carrying a payload of 10 kg. Finally, we perform experiments on impedance control and mechanical robustness by studying the response of the robotics testbed to hammering impacts and external force interactions.

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Team RoboSimian: Semi‐autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

Cited by 68 publications

References 39 publications

Training in Task Space to Speed Up and Guide Reinforcement Learning

Training in Task Space to Speed Up and Guide Reinforcement Learning

Autonomous car driving by a humanoid robot

Investigations of a Robotic Test Bed With Viscoelastic Liquid Cooled Actuators

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