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Found 15 papers, 2 papers with code
Event-Triggered Polynomial Control for Trajectory Tracking of Unicycle Robots
In this control method, each control input signal between two consecutive events is a polynomial.
Stochastic Action Prediction for Imitation Learning
Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions.
Translating Natural Language Instructions to Computer Programs for Robot Manipulation
It is highly desirable for robots that work alongside humans to be able to understand instructions in natural language.
Spatial Reasoning from Natural Language Instructions for Robot Manipulation
Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language.
One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks
A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects.
Multi-Instance Aware Localization for End-to-End Imitation Learning
Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited.
Scene Text Detection for Augmented Reality -- Character Bigram Approach to reduce False Positive Rate
Natural scene text detection is an important aspect of scene understanding and could be a useful tool in building engaging augmented reality applications.
Imitation Learning for High Precision Peg-in-Hole Tasks
Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day.
Teaching Robots Novel Objects by Pointing at Them
Robots that must operate in novel environments and collaborate with humans must be capable of acquiring new knowledge from human experts during operation.
Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach
In particular, the parameters of the end-foot trajectories are shaped via a linear feedback policy that takes the torso orientation and the terrain slope as inputs.
Robust and Scalable Techniques for TWR and TDoA based localization using Ultra Wide Band Radios
Current trends in autonomous vehicles and their applications indicates an increasing need in positioning at low battery and compute cost.
Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations
First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius.
Vermillion: A High-Performance Scalable IoT Middleware for Smart Cities
In this paper, we present "Vermillion'', a scalable, secure and open-source IoT middleware for smart cities which provides in-built support for scaling-out.
Distributed, Parallel, and Cluster Computing
Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots
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.
Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives
Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs.
