Augmented Random Search (RL Algorithm)

Augmented Random Search (RL Algorithm)

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

With the research into development of quadruped robots picking up pace, learning based techniques are being explored for developing locomotion controllers for such robots. A key problem is to generate leg trajectories for continuously varying target linear and angular velocities, in a stable manner. In this paper, we propose a two pronged approach to address this problem. First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius. These policies are then augmented using a higher level neural network for handling the transition between the learned trajectories. Specifically, we develop a neural network based filter that takes in target velocity, radius and transforms them into new commands that enable smooth transitions to the new trajectory. This transformation is achieved by learning from expert demonstrations. An application of this is the transformation of a novice user’s input into an expert user’s input, thereby ensuring stable manoeuvres regardless of the user’s experience. Training our proposed architecture requires much less expert demonstrations compared to standard neural network architectures. Finally, we demonstrate experimentally these results in the in-house quadruped Stoch 2.

In this paper, with a view toward fast deployment of learned locomotion gaits in low-cost hardware, we generate a library of walking trajectories, namely, forward trot, backward trot, side-step, and turn in our custom built quadruped robot, Stoch 2, using reinforcement learning. There are existing approaches that determine optimal policies for each time step, whereas we determine an optimal policy, in the form of endfoot trajectories, for each half walking step i.e., swing phase and stance phase. The way-points for the foot trajectories are obtained from a linear policy, i.e., a linear function of the states of the robot, and cubic splines are used to interpolate between these points. Augmented Random Search, a modelfree and gradient-free learning algorithm, is used to learn the policy in simulation. This learned policy is then deployed on hardware, yielding a trajectory in every half walking step. Different locomotion patterns are learned in simulation by enforcing a preconfigured phase shift between the trajectories of different legs. Transition from one gait to another is achieved by using a low-pass filter for the phase, and the sim-to-real transfer is improved by a linear transformation of the states obtained through regression

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Gait Library Synthesis Learning Stable Manuveuers


          title={Gait Library Synthesis for Quadruped Robots via Augmented Random Search},
          author={Tirumala, Sashank and Sagi, Aditya and Paigwar, Kartik and Joglekar, Ashish and Bhatnagar, Shalabh and Ghosal, Ashitava and Amrutur, Bharadwaj and Kolathaya, Shishir},
          journal={arXiv preprint arXiv:1912.12907},
          title={Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations},
          author={Tirumala, Sashank and Gubbi, Sagar and Paigwar, Kartik and Sagi, Aditya and Joglekar, Ashish and Bhatnagar, Shalabh and Ghosal, Ashitava and Amrutur, Bharadwaj and Kolathaya, Shishir},
          journal={arXiv preprint arXiv:2007.14290},

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Aditya Sagi
Aditya Sagi
Technical Consultant
Shishir Kolathaya (HEAD)
Assistant Professor
Last updated: 2020-10-08