We describe our full body humanoid control approach developed for the simulation phase of the DARPA Robotics Challenge (DRC), as well as the modifications made for the DARPA Robotics Challenge Trials. We worked with the Boston Dynamics Atlas robot. Our approach was initially targeted at walking, and it consisted of two levels of optimization: a high-level trajectory optimizer that reasons about center of mass and swing foot trajectories, and a low-level controller that tracks those trajectories by solving floating base full body inverse dynamics using quadratic programming. This controller is capable of walking on rough terrain, and it also achieves long footsteps, fast walking speeds, and heel-strike and toe-off in simulation. During development of these and other whole body tasks on the physical robot, we introduced an additional optimization component in the low-level controller, namely an inverse kinematics controller. Modeling and torque measurement errors and hardware features of the Atlas robot led us to this three-part approach, which was applied to three tasks in the DRC Trials in December 2013. C 2014 Wiley Periodicals, Inc.
One popular approach to controlling humanoid robots is through inverse kinematics (IK) with stiff joint position tracking. On the other hand, inverse dynamics (ID) based approaches have gained increasing acceptance by providing compliant motions and robustness to external perturbations. However, the performance of such methods is heavily dependent on high quality dynamic models, which are often very difficult to produce for a physical robot. IK approaches only require kinematic models, which are much easier to generate in practice. In this paper, we supplement our previous work with ID-based controllers by adding IK, which helps compensate for modeling errors. The proposed full body controller is applied to three tasks in the DARPA Robotics Challenge (DRC) Trials in Dec. 2013.
This study addresses the problem of learning robust frame-level feature representation for unsupervised subword modeling in the zero-resource scenario. Robustness of the learned features is achieved through effective speaker adaptation and exploiting cross-lingual phonetic knowledge. For speaker adaptation, an out-of-domain automatic speech recognition (ASR) system is used to estimate fMLLR features for untranscribed speech of target zero-resource languages. The fMLLR features are applied in multi-task learning of a deep neural network (DNN) to further obtain phonetically discriminative and speaker-invariant bottleneck features (BNFs). Frame-level labels for DNN training can be acquired based on two approaches: Dirichlet process Gaussian mixture model (DPGMM) clustering, and outof-domain ASR decoding. Moreover, system fusion is performed by concatenating BNFs extracted by different DNNs. Our methods are evaluated by ZeroSpeech 2017 Track one, where the performance is evaluated by ABX minimal pair discriminability. Experimental results demonstrate that: (1) Using an out-of-domain ASR system to perform speaker adaptation of zero-resource speech is effective and efficient; (2) Our system achieves highly competitive performance to state of the art; (3) System fusion could improve feature representation capability.
The DARPA Robotics Challenge (DRC) requires teams to integrate mobility, manipulation, and perception to accomplish several disaster-response tasks. We describe our hardware choices and software architecture, which enable human-in-the-loop control of a 28 degree-of-freedom ATLAS humanoid robot over a limited bandwidth link. We discuss our methods, results, and lessons learned for the DRC Trials tasks. The effectiveness of our system architecture was demonstrated as the WPI-CMU DRC Team scored 11 out of a possible 32 points, ranked seventh (out of 16) at the DRC Trials, and was selected as a finalist for the DRC Finals. C 2014 Wiley Periodicals, Inc.
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