Design, Development and Experimental Realization of A Quadrupedal Research Platform: Stoch

Dholakiya, Dhaivat; Bhattacharya, Shounak; Gunalan, Ajay; Singla, Abhik; Bhatnagar, Shalabh; Amrutur, Bharadwaj; Ghosal, Ashitava; Kolathaya, Shishir

doi:10.1109/iccar.2019.8813480

Cited by 14 publications

(20 citation statements)

References 13 publications

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“…2. The calculation of the leg work-space and it's inverse kinematic details can be found in [2]. In this paper, we focus on realizing trajectories of the feet in Cartesian coordinates on a fixed plane.…”

Section: A Kinematic Descriptionmentioning

confidence: 99%

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

Tirumala

Gubbi²,

Paigwar³

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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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.

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Section: A Kinematic Descriptionmentioning

confidence: 99%

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

Tirumala

Gubbi²,

Paigwar³

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Self Cite

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“…The electric power to the robot is supplied from Li-Po batteries. More details about the hardware are described in [23]. total .…”

Section: A Hardware Description Of Stochmentioning

confidence: 99%

Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives

Singla

Bhattacharya

Dholakiya

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Humans and animals are believed to use a very minimal set of trajectories to perform a wide variety of tasks including walking. Our main objective in this paper is two fold 1) Obtain an effective tool to realize these basic motion patterns for quadrupedal walking, called the kinematic motion primitives (kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2) Realize a set of behaviors, namely trot, walk, gallop and bound from these kinematic motion primitives in our custom four legged robot, called the "Stoch". D-RL is a data driven approach, which has been shown to be very effective for realizing all kinds of robust locomotion behaviors, both in simulation and in experiment. On the other hand, kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors. We first generate walking gaits from D-RL, which uses policy gradient based approaches. We then analyze the resulting walking by using principal component analysis. We observe that the kMPs extracted from PCA followed a similar pattern irrespective of the type of gaits generated. Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs. This type of methodology improves the transferability of these gaits to real hardware, lowers the computational overhead on-board, and also avoids multiple training iterations by generating a set of derived behaviors from a single learned gait.

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“…It is the second generation robot in the Stoch series. A detailed description of the previous model can be found in [17], [20]. In this section, we first describe the overview of Stoch 2 robot and the essential hardware details, and then describe the simulation framework used for training.…”

Section: Simulation Model Designmentioning

confidence: 99%

“…2a. The overall size and form factor of the robot is similar to Stoch [20]. Each body module is composed of two legs.…”

Section: Simulation Model Designmentioning

confidence: 99%

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Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

Bhattacharya

Singla

Abhimanyu

et al. 2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1 m/s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.

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Design, Development and Experimental Realization of A Quadrupedal Research Platform: Stoch

Cited by 14 publications

References 13 publications

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives

Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

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