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portfolio
Data-Driven Gait Synthesis
How do we know what control inputs to use to get a robot to successfully move around in a given environment? For traditional rigid robots, we use established kinematics and dynamics equations to apply model-based control. But this isn’t so simple for soft robots, whose sensivity to manufacturing innacuries and environmental forces alongisde complicated dynamics hinder the feasibility and accuracy of traditional approaches. Instead, we can use a reinforcement learning-based approach, where we collect data on repeated robot motions which are then fed into an optimization algorithm. The result is optimal translation and rotation gaits that are found without using an explicit model of the robot. We can even use these gaits to achieve real-time path planning with obstacle avoidance. 
Design Optimization of Modular Reconfigurable Soft Robots
Increasing the functionality, adpatability, and versaility of robots usually means increasing the cost – think of the complex and expensive robots advertised in the news. Alternatively, we can use innovative methods to design and build low-cost robots capable of exploring complex environments. In this research, we focus on two solutions: soft robots and modular reconfigurable robots. Soft robots are able to passively adapt to environmental forces in a way that simplifies control, reduces the required number of actuators, and enables relatively simple designs to exhibit complex behavior. Then, we can build a collection (or swarm) of many soft robots designed to both function individually and combine together into larger robots, which we refer to as modular reconfigurable robots. Figuring out how to design and control these systems ins’t trivial. Nevertheless, we can use modern CAD software and map projection formulas to optimize the design of the robots such that multiple robots can both move individually and combine together to form a ball. 
publications
Shape-centric Modeling for Soft Robot Inchworm Locomotion,
Published in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Abstract: Abstract: Soft robot modeling tends to prioritize soft robot dynamics in order to recover how they might behave. Soft robot design tends to focus on how to…
Recommended citation: A. N. Mahendran, C. Freeman, A. H. Chang, M. McDougall, P. A. Vela, and V. Vikas, “Multi-Gait Locomotion Planning and Tracking for Tendon-Actuated Terrestrial Soft Robot (TerreSoRo),” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 2542-2549, doi: 10.1109/IROS55552.2023.10341926.
Topology and morphology design of spherically reconfigurable homogeneous Modular Soft Robots (MSoRos)
Published in Soft Robotics, 2023
Abstract: Imagine a swarm of terrestrial robots that can explore an environment, and, on completion of this task, reconfigure into a spherical ball and roll out. This dimensional…
Recommended citation: Freeman, Caitlin, Michael Maynard, and Vishesh Vikas. “Topology and Morphology Design of Spherically Reconfigurable Homogeneous Modular Soft Robots.” Soft Robotics, vol. 10, no. 1, pp. 52–65, 2023, doi: 10.1089/soro.2021.0125.
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Topology Design and Optimization of Modular Soft Robots Capable of Homogenous and Heterogenous Reconfiguration
Published in ASME Journal of Computational and Nonlinear Dynamics, 2023
Abstract: The deformability of soft material robots provides them with the ability to transform between complex shapes and forms. This unique ability facilitates…
Recommended citation: C. Freeman, J. Conzola, and V. Vikas, “Topology Design and Optimization of Modular Soft Robots Capable of Homogenous and Heterogenous Reconfiguration,” Journal of Computational and Nonlinear Dynamics, vol. 18, no. 6, Apr. 2023, doi: 10.1115/1.4062265.
Multi-Gait Locomotion Planning and Tracking for Tendon-Actuated Terrestrial Soft Robot (TerreSoRo)
Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
Abstract: The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise…
Recommended citation: A. N. Mahendran, C. Freeman, A. H. Chang, M. McDougall, P. A. Vela, and V. Vikas, “Multi-Gait Locomotion Planning and Tracking for Tendon-Actuated Terrestrial Soft Robot (TerreSoRo),” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 2542-2549, doi: 10.1109/IROS55552.2023.10341926.
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Environment-Centric Learning Approach for Gait Synthesis in Terrestrial Soft Robots
Published in IEEE Transactions on Robotics, 2025
Abstract: Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment…
Recommended citation: C. Freeman, A. N. Mahendran, and V. Vikas, “Environment-Centric Learning Approach for Gait Synthesis in Terrestrial Soft Robots,” in IEEE Transactions on Robotics, vol. 41, pp. 2144-2163, 2025, doi: 10.1109/TRO.2025.3548543.
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talks
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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