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Posts
Blog Post number 4
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Blog Post number 1
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publications
Trajectory Tracking and Control of Car-Like Robots
Published in Machines, Mechanism and Robotics, 2018
This paper deals with trajectory tracking control of a car-like robot. By exploiting the differential flatness property of the system based on the dynamics, a trajectory tracking controller using flatness-based control techniques is designed. A singularity in the system for the chosen control inputs, which does not allow direct application of feedback linearization control, is identified and this singularity is overcome by applying the dynamics-extension algorithm to obtain a dynamic feedback linearized controller. This controller results in asymptotic tracking convergence of the system’s trajectory to the reference trajectory. Through numerical simulations, the control system is shown to track prescribed trajectories satisfactorily even in the presence of parametric uncertainties.
Recommended citation: S Shetty, A Ghosal, Trajectory Tracking and Control of Car-Like Robots, 2019 Machines, Mechanism and Robotics pp 759-767 https://link.springer.com/chapter/10.1007/978-981-10-8597-0_65
Trajectory based deep policy search for quadrupedal walking
Published in RO-MAN, 2019
In this paper, we explore a specific form of deep reinforcement learning (D-RL) technique for quadrupedal walking—trajectory based policy search via deep policy networks. Existing approaches determine optimal policies for each time step, whereas we propose to determine an optimal policy for each walking step. We justify our approach based on the fact that animals including humans use “low” dimensional trajectories at the joint level to realize walking. We will construct these trajectories by using Bezier polynomials, with the coefficients being determined by a parameterized policy. In order to maintain smoothness of the trajectories during step transitions, hybrid invariance conditions are also applied. The action is computed at the beginning of every step, and a linear PD control law is applied to track at the individual joints. After each step, reward is computed, which is then used to update the new policy parameters for the next step. After learning an optimal policy, i.e., an optimal walking gait for each step, we then successfully play them in a custom built quadruped robot, Stoch 2, thereby validating our approach.
Recommended citation: S Kolathaya, A Joglekar, S Shetty, D Dholakiya, A Sagi, S Bhattacharya, A Singla, S Bhatnagar, A Ghosal, B Amrutur, Trajectory based deep policy search for quadrupedal walking, 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) https://ieeexplore.ieee.org/document/8956369
Trajectory Prediction with Compressed 3D Environment Representation using Tensor Train Decomposition
Published in ICAR, 2021
Trajectory optimization for motion planning is a fundamental problem in robotics. Algorithms typically require good initialization in order to find optimal trajectories. To provide such initialization, many approaches rely on the concept of memory of motion, where a function approximator is trained on a database of robot trajectories to predict good initial trajectories for novel situations, and hence speeding up the subsequent trajectory optimization process. To be able to generalize well to new environment, an expressive environment descriptor is necessary. We propose to encode the environment by discretized signed distance functions (SDF) which are then compressed using a tensor train (TT) decomposition approach. In order to show the expressiveness of this low-rank TT-SDF representation, three function approximators are compared: a nearest neighbor predictor, an artificial neural network and a mixture density network. We demonstrate the proposed method with motion planning examples on two different systems (point-mass and quadcopter). Our experiments demonstrate that the TT-SDF encoding can provide meaningful environment descriptors in order to predict good motion trajectories for warm-starting an optimal control solver.
Recommended citation: Brudermüller, L., Lembono, T.S., Shetty, S. and Calinon, S. (2021). Trajectory Prediction with Compressed 3D Environment Representation using Tensor Train Decomposition. In Proc. IEEE Intl Conf. on Advanced Robotics (ICAR), pp. 633-639 https://ieeexplore.ieee.org/document/9659407
Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
Published in IEEE T-RO, 2022
By generating control policies that create natural search behaviors, ergodic control provides a principled solution to address tasks that require exploration. Since a large class of ergodic control algorithms relies on spectral analysis, they suffer from the curse-of-dimensionality, both in storage and computation. This drawback has prohibited the application of ergodic control in robot manipulation since it often requires exploration in state space with more than 2 dimensions. Indeed, the original ergodic control formulation will typically not allow exploratory behaviors to be generated for a complete 6D end-effector pose. In this research work, we propose a solution for ergodic exploration in multidimensional spaces using low-rank tensor approximation techniques. We rely on tensor train decomposition, a recent approach from multilinear algebra for low-rank approximation and efficient computation of multidimensional arrays. The proposed solution is efficient both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. We leverage our algorithm for high-dimensional ergodic exploration to solve the peg-in-hole insertion problem. We model peg-in-hole insertion task as a target detection problem and use our ergodic controller for the exploration in the 6D state space of the robot end-effector. The approach is applied to a peg-in-hole insertion task using a 7-axis Franka Emika Panda robot, where ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors but only using human demonstrations. The approach can handle uncertainties in the location of the hole and/or graspings of the peg that typically exists in insertion tasks.
Recommended citation: S Shetty, J Silvério, S Calinon, Ergodic Exploration using Tensor Train: Applications in Insertion Tasks, IEEE Transactions on Robotics (T-RO) https://sites.google.com/view/ergodic-exploration/
Tensor Train for Global Optimization Problems in Robotics
Published in IJRR 2023, 2023
The convergence of many numerical optimization techniques is highly sensitive to the initial guess provided to the solver. We propose an approach based on low-rank tensor approximation techniques to initialize the existing optimization solvers close to the optima. The approach uses only the definition of the cost function (no gradient information is required) and does not need access to any database of good solutions. This renders the method less susceptible to getting stuck in poor local optima as compared to gradient-based methods. Unlike existing approaches in robotics that solve each optimization problem corresponding to a given task from scratch, we parameterize the optimization problem with respect to the tasks. We first transform the cost function, which is a function of task parameters and optimization variables, into an unnormalized probability density function. Then for a given task, we generate samples from the conditional distribution with respect to the given task parameter and use them as initialization for the optimization solver. As conditioning and sampling from an arbitrary density function is challenging, we use Tensor Train decomposition to obtain a surrogate probability model from which we can efficiently obtain the conditional model and the samples. The proposed method can produce multiple solutions coming from different modes (when they exist) for a given task. We first evaluate the approach by applying it to various challenging benchmark functions for numerical optimization that are difficult to solve using gradient-based optimization solvers with a naive initialization, showing that the proposed method can produce samples close to the global optima and coming from multiple modes. We then demonstrate the generality of the framework and its relevance to robotics by applying the proposed method to inverse kinematics and motion planning problems with a 7-DoF manipulator, as commonly encountered in robot manipulation.
Recommended citation: S Shetty, T Lembono, T Loew, S Calinon, Tensor Train for Global Optimization Problems in Robotics, arXiv:2206.05077 https://sites.google.com/view/ttgo/home
Dynamic Programming using Tensor Approximation for Contact-rich Manipulation
Published in Workshop on Embracing Contacts, IEEE ICRA 2023, 2023
We present a tensor approximation approach to dynamic programming for contact-rich manipulation tasks, efficiently handling hybrid dynamics that arise from making and breaking contact.
Recommended citation: T Xue*, S Shetty*, and S Calinon, Dynamic Programming using Tensor Approximation for Contact-rich Manipulation, Workshop on Embracing Contacts, IEEE ICRA, 2023. https://openreview.net/pdf?id=nhiMzuaPoP
Robust Execution of Assembly Policies Using a Pose Invariant Task Representation
Published in Intl Conf. on Ubiquitous Robots (UR) 2023, 2023
A pose invariant task representation for robust execution of assembly policies in robotic manipulation.
Recommended citation: B Nemec, M Hrovat, M Simonic, S Shetty, S Calinon, and A Ude, Robust Execution of Assembly Policies Using a Pose Invariant Task Representation, In Proc. Intl Conf. on Ubiquitous Robots (UR), pp. 779-786, 2023.
Generalized Policy Iteration using Tensor Approximation for Hybrid Control
Published in ICLR 2024 (Spotlight, top 5%), 2024
We propose a novel approach for solving hybrid control problems using tensor approximation within the generalized policy iteration framework. Our method addresses systems with both continuous and discrete state-action spaces, which are common in contact-rich manipulation tasks. By leveraging tensor train decomposition, we efficiently represent value functions and policies over hybrid domains, overcoming the curse of dimensionality that plagues traditional dynamic programming approaches.
Recommended citation: S Shetty, T Xue, and S Calinon, Generalized Policy Iteration using Tensor Approximation for Hybrid Control, International Conference on Learning Representations (ICLR), 2024. [Spotlight Paper, 5% acceptance rate] https://sites.google.com/view/ttpi4control/home
Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning
Published in RSS 2024, 2024
An optimization-based approach to sequential skill planning that combines logic-based task planning with continuous skill optimization for robotic manipulation.
Recommended citation: T Xue, A Razmjoo, S Shetty, and S Calinon, Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning, In Proc. Robotics: Science and Systems (RSS), 2024.
Robust Manipulation Primitive Learning via Domain Contraction
Published in CoRL 2024, 2024
A method for learning robust manipulation primitives via domain contraction, enabling reliable skill execution under perturbations and uncertainties.
Recommended citation: T Xue, A Razmjoo, S Shetty, and S Calinon, Robust Manipulation Primitive Learning via Domain Contraction, In Proc. Conference on Robot Learning (CoRL), 2024.
Sampling-Based Constrained Motion Planning with Products of Experts
Published in IJRR 2025, 2025
A sampling-based approach to constrained motion planning using products of experts for robotic manipulation tasks.
Recommended citation: A Razmjoo, T Xue, S Shetty, and S Calinon, Sampling-Based Constrained Motion Planning with Products of Experts, International Journal of Robotics Research (IJRR), 2025.
Robust Contact-rich Manipulation through Implicit Motor Adaptation
Published in IJRR 2025, 2025
A method for robust contact-rich manipulation through implicit motor adaptation, enabling reliable performance under varying contact conditions.
Recommended citation: T Xue, A Razmjoo, S Shetty, and S Calinon, Robust Contact-rich Manipulation through Implicit Motor Adaptation, International Journal of Robotics Research (IJRR), 2025.
talks
An introduction to speech-based technologies for Natural Language Processing applications
Published:
Since the last two decades, the amount of data generated and collected has grown exponentially, and especially through the rise of unstructured data such as images, videos or text. More recently, audio and speech data have gained a large interest, for example through voice assistants. Companies like Google, Facebook, Apple, and Amazon have shown an increasing interest in professionals with skills and tools for ‘understanding’ and ‘transforming’ the massive flow of speech data in relevant information. Some of the most important speech-based technologies are voice activity detection, speaker diarization and identification, and automatic speech recognition. These techologies are often used as an input to various NLP applications afterwards. This brief workshop will give you a set of basic tools for grasping the main aspects of speech-based technologies and how they can be implemented in real-life cases.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.