Author name cluster

Avinash Siravuru

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

RLDM Conference 2017 Conference Abstract

Multi-modal Deep Reinforcement Learning with a Novel Sensor-based Dropout

Guan-Horng Liu
Avinash Siravuru
Sai Prab-
Manuela Veloso
George Kantor

Sensor fusion is a key driver in the success of autonomous driving, given how instrumental it is to improve accuracy and robustness in the vehicle’s algorithmic decision making. However, in the space of end-to-end sensorimotor control, this multi-modal outlook has not received much attention. In the interest of enhancing safety and accuracy in control, a multi-modal approach to end-to-end autonomous navigation is need of the hour. Here, we introduce Multi-modal Deep Reinforcement Learning, and demonstrate how the use of multiple sensors improves the reward for an agent. For this purpose, we augment using both DDPG and NAF algorithms to admit multiple sensor input. The efficacy of a multi-modal policy is shown through extensive simulations experiments in TORCS, a popular open-source racing car game. Additionally, we introduce a new stochastic regularization technique, called Sensor Dropout to reduces the network’s sensitivity to any one sensor. Suitable metrics have been devised to study this behavior and highlight its applicability to other domains that operate in multi-modal settings.

PDF Details

IROS Conference 2016 Conference Paper

Multirobot sequential composition

Glenn Wagner
Howie Choset
Avinash Siravuru

Conventional path planning algorithms compute a single path through the configuration space. There is no guarantee that a physical robot will be able to track the trajectory while avoiding collisions, particularly in the presence of environmental perturbations and errors in the process model. Sequential composition combines planning and control by computing a sequence of controllers to execute rather than a single trajectory, offering greater safety guarantees. In this paper, we apply sequential composition to multirobot systems in a scalable fashion using M*, an advanced multirobot path planning algorithm. Controllers will vary in size and geometry, and thus take different amounts of time to execute. To handle these differences, we introduce the time augmented joint prepares graph and the approximate time augmented joint prepares graph which simplifies implementation by discretizing time. We validate our approach in a mixed reality test framework.

Details

IROS Conference 2016 Conference Paper

Optimal control for geometric motion planning of a robot diver

Roberto Shu
Avinash Siravuru
Akshara Rai
Tony Dear
Koushil Sreenath
Howie Choset

Inertial reorientation of airborne articulated bodies has been an active area of research in the robotics community, as this behavior can help guide dynamic robots to a safe landing with minimal damage. The main objective of this work is emulating the aggressive and large angle correction maneuvers, like somersaults, that are performed by human divers. To this end, a planar three link robot, called DiverBot, is proposed. By considering a gravity-free scenario, a local connection is obtained between joint angles and the body orientation, resulting in a reduction in the system dynamics. An optimal control policy applied on this reduced configuration space yielded diving maneuvers that are dynamically feasible. Numerical results show that the DiverBot can execute one somersault without drift and multiple somersaults with minimal drift.

Details

IROS Conference 2015 Conference Paper

Stair Climbing using a compliant modular robot

Sri Harsha Turlapati
Mihir Shah
Phani-Teja Singamaneni
Avinash Siravuru
Suril Vijaykumar Shah
K. Madhava Krishna

Stair Climbing is a key functionality desired for robots deployed in Urban Search and Rescue (USAR) scenarios. A novel compliant modular robot was proposed earlier to climb steep and big obstacles. This work extends the functionality of this robot to ascend and descend stairs of dimensions that are also typical of an urban setting. Stair Climbing is realized by equipping the robot's link joints with optimally designed passive spring pairs that resist clockwise and counter clockwise moments generated by the ground during the climbing motion. This 3-module robot is only propelled by wheel actuators. Desirable stair climbing configurations are estimated a-priori and used to obtain the optimal stiffness for springs. Extensive numerical simulation results over different stair configurations are shown. The numerical simulations are corroborated by experimentation using the prototype and its performance is tabulated for different types of surfaces.

Details

ICRA Conference 2014 Conference Paper

A compliant multi-module robot for climbing big step-like obstacles

Avinash Siravuru
Ankur Srivastava
Akshaya Purohit
Suril Vijaykumar Shah
K. Madhava Krishna

A novel compliant robot is proposed for traversing on unstructured terrains. The robot consists of modules, each containing a link and an active wheel-pair, and neighboring modules are connected using a passive joint. This type of robots are lighter and provide high durability due to the absence of link-actuators. However, they have limited climbing ability due to tendency of tipping over while climbing big obstacles. To overcome this disadvantage, the use of compliant joints is proposed in this work. Stiffness of each compliant joint is estimated by formulating an optimization problem with an objective to minimize link joint moments while maintaining static-equilibrium. This is one of the key novelties of the proposed work. A design methodology is also proposed for developing an n-module compliant robot for climbing a given height on a known surface. The efficacy of the proposed formulation is illustrated using numerical simulations of the three and five module robots. The robot is successfully able to climb maximum heights upto three times and six times the wheel diameter using three and five modules, respectively. A working prototype was developed and the simulation results were successfully validated on it.

Details