YA's Blog

Legged robots are an attractive option to wheeled robots primarily for applications in rough terrain and difficult, cluttered environments. The freedom to choose contact points enables such robots to interact with the environment while avoiding obstacles effectively. With such capabilities, legged robots may be applied to application such as rescuing people in mountains and forests, climbing stairs, carrying payloads in construction sites, and inspecting unstructured under-ground tunnels. Designing dynamic and agile locomotion for such robots is a longstanding research problem because it is difficult to control an under-actuated robot performing highly dynamic motion that requires tricky balance. The recent advances in Deep Reinforcement Learning (DRL) has made it possible to learn robot locomotion policies from scratch without any human intervention. In this post, we discuss various research directions where DRL can be employed to solve locomotion problems in quadruped robots. Si

Reinforcement learning has gained popularity after the success of agents playing Atari games at superhuman level. Applying RL for solving intelligent decision making problems in robotics is a key to building smarter robots. In this post, we briefly discuss how an MDP can be defined for a robot and talk about Deep Reinforcement learning and its shortcomings. Markov Decision Process(MDP) Reinforcement learning agents learn actions from scratch through their interactions with the environment. There are four basic components of a reinforcement learning system: states, actions, reward and policy. The state in RL is defined by the current configuration or the state of the robot. For example, the current sensor readings such as motor encoder values or position and orientation of the centre of mass of the robot can be taken to define the "state space". The actions comprises of the possible motions of the robot in response to its interaction with the environment. The action space