Do you want to publish a course? Click here

Learning Symmetric and Low-energy Locomotion

115   0   0.0 ( 0 )
 Added by Wenhao Yu
 Publication date 2018
and research's language is English




Ask ChatGPT about the research

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning.



rate research

Read More

The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts the learning difficulty and the resulting performance. We compare the impact of four different action parameterizations (torques, muscle-activations, target joint angles, and target joint-angle velocities) in terms of learning time, policy robustness, motion quality, and policy query rates. Our results are evaluated on a gait-cycle imitation task for multiple planar articulated figures and multiple gaits. We demonstrate that the local feedback provided by higher-level action parameterizations can significantly impact the learning, robustness, and quality of the resulting policies.
Learning robotic manipulation through reinforcement learning (RL) using only sparse reward signals is still considered a largely unsolved problem. Leveraging human demonstrations can make the learning process more sample efficient, but obtaining high-quality demonstrations can be costly or unfeasible. In this paper we propose a novel approach that introduces object-level demonstrations, i.e. examples of where the objects should be at any state. These demonstrations are generated automatically through RL hence require no expert knowledge. We observe that, during a manipulation task, an object is moved from an initial to a final position. When seen from the point of view of the object being manipulated, this induces a locomotion task that can be decoupled from the manipulation task and learnt through a physically-realistic simulator. The resulting object-level trajectories, called simulated locomotion demonstrations (SLDs), are then leveraged to define auxiliary rewards that are used to learn the manipulation policy. The proposed approach has been evaluated on 13 tasks of increasing complexity, and has been demonstrated to achieve higher success rate and faster learning rates compared to alternative algorithms. SLDs are especially beneficial for tasks like multi-object stacking and non-rigid object manipulation.
We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method for quadrupedal locomotion that leverages a Transformer-based model for fusing proprioceptive states and visual observations. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We show that our method obtains significant improvements over policies with only proprioceptive state inputs, and that Transformer-based models further improve generalization across environments. Our project page with videos is at https://RchalYang.github.io/LocoTransformer .
The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.
Snake robots, comprised of sequentially connected joint actuators, have recently gained increasing attention in the industrial field, like life detection in narrow space. Such robots can navigate through the complex environment via the cooperation of multiple motors located on the backbone. However, controlling the robots in an unknown environment is challenging, and conventional control strategies can be energy inefficient or even fail to navigate to the destination. In this work, a snake locomotion gait policy is developed via deep reinforcement learning (DRL) for energy-efficient control. We apply proximal policy optimization (PPO) to each joint motor parameterized by angular velocity and the DRL agent learns the standard serpenoid curve at each timestep. The robot simulator and task environment are built upon PyBullet. Comparing to conventional control strategies, the snake robots controlled by the trained PPO agent can achieve faster movement and more energy-efficient locomotion gait. This work demonstrates that DRL provides an energy-efficient solution for robot control.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا