Michael Lutter, Christian Ritter & Jan Peters, Department of Computer Science Technische Universität Darmstadt
Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data. For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems. In particular, learning physics models for model-based control requires robust extrapolation from fewer samples – often collected online in real-time – and model errors may lead to drastic damages of the system.
Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples. As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed. DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility.
The resulting DeLaN network performs very well at robot tracking control. The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time.
A new paper using PyBullet from ETH Zurich (Michel Breyer, Fadri Furrer, Tonci Novkovic, Roland Siegwart, and Juan Nieto)
Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects’ variability. This paper presents a comparison of different reinforcement learning-based approaches for object picking with a robotic manipulator. We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem. For efficient and more flexible data collection, we train in simulation and transfer the policies to a real robot. We show that using curriculum learning, policies learned with a sparse reward formulation can be trained at similar rates as with a shaped reward. These policies result in success rates comparable to the policy initialized on the simplified task. We could successfully transfer these policies to the real robot with only minor modifications of the depth image filtering. We found that using a heuristic to warm-start the training was useful to enforce desired behavior, while the policies trained from scratch using a curriculum learned better to cope with unseen scenarios where objects are removed.
A while ago, Our RSS 2018 paper “Sim-to-Real: Learning Agile Locomotion For Quadruped Robots” is accepted! (with Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, Vincent Vanhoucke).
The Gibson project, by Stanford University AI Lab uses PyBullet:
Perception and being active (i.e. having a certain level of motion freedom) are closely tied. Learning active perception and sensorimotor control in the physical world is cumbersome as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning in simulation which consequently casts a question on transferring to real-world. In this paper, we study learning perception for active agents in real-world, propose a virtual environment for this purpose, and demonstrate complex learned locomotion abilities. The primary characteristics of the learning environments, which transfer into the trained agents, are I) being from the real-world and reflecting its semantic complexity, II) having a mechanism to ensure no need to further domain adaptation prior to deployment of results in real-world, III) embodiment of the agent and making it subject to constraints of space and physics.
Rocket League integrated Bullet Physics in the Unreal engine for deterministic networked physics simulation.
‘Rocket League’ owes a large part of its success to how fun it is to play for people of all ages. But why does the game feel so good? One of Psyonix’s main programmers, Jared Cone, will take session attendees through an inside look at the specific game design decisions and implementation details that made ‘Rocket League’ and its networked physics the success that it is today!
A paper from Google Brain Robotics using PyBullet:
Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback. In this paper, we study how viewpoint-invariant visual servoing skills can be learned automatically in a robotic manipulation scenario. To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object. The problem that must be solved by this controller is fundamentally ambiguous: under severe variation in viewpoint, it may be impossible to determine the actions in a single feedforward operation. Instead, our visual servoing system must use its memory of past movements to understand how the actions affect the robot motion from the current viewpoint, correcting mistakes and gradually moving closer to the target. This ability is in stark contrast to most visual servoing methods, which either assume known dynamics or require a calibration phase. We show how we can learn this recurrent controller using simulated data and a reinforcement learning objective. We then describe how the resulting model can be transferred to a real-world robot by disentangling perception from control and only adapting the visual layers. The adapted model can servo to previously unseen objects from novel viewpoints on a real-world Kuka IIWA robotic arm.
A new paper from Google Brain and X using PyBullet:
earning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.
Bullet 2.87 has improved support for robotics, reinforcement learning and VR. In particular, see the “Reinforcement Learning” section in the pybullet quickstart guide at http://pybullet.org . There are also preliminary C# bindings to allow the use of pybullet inside Unity 3D for robotics and reinforcement learning. In addition, vectorunit Beach Buggy Racing using Bullet has been released for the Nintendo Switch!