Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN

Learning Needle Pick-And-Place without expert demonstrations

We introduce a novel approach for learning a complex multi-stage needle pick-and-place manipulation task for surgical applications using Reinforcement Learning without expert demonstrations or explicit curriculum. The proposed method is based on a recursive decomposition of the original task into a sequence of sub-tasks with increasing complexity and utilizes an actor-critic algorithm with deterministic policy output. In this work, exploratory bottlenecks have been used by a human expert as convenient boundary points for partitioning complex tasks into simpler subunits. Our method has successfully learnt a policy for the needle pick-and-place task, whereas the state-of-the-art TD3+HER method is unable to achieve success without the help of expert demonstrations. Comparison results show that our method achieves the highest performance with a 91% average success rate

Local Navigation Among Movable Obstacles with Deep Reinforcement Learning

Autonomous robots would benefit a lot by gaining the ability to manipulate their environment to solve path planning tasks, known as the Navigation Among Movable Obstacle (NAMO) problem. In this paper, we present a deep reinforcement learning approach for solving NAMO locally, near narrow passages. We train parallel agents in physics simulation using an Advantage Actor-Critic based algorithm with a multi-modal neural network. We present an online policy that is able to push obstacles in a non-axial-aligned fashion, react to unexpected obstacle dynamics in real-time, and solve the local NAMO problem. Experimental validation in simulation shows that the presented approach generalises to unseen NAMO problems in unknown environments. We further demonstrate the implementation of the policy on a real quadrupedal robot, showing that the policy can deal with real-world sensor noises and uncertainties in unseen NAMO tasks.Comment: 7 pages, 7 figures, 4 table

TeLeMan: Teleoperation for Legged Robot Loco-Manipulation using Wearable IMU-based Motion Capture

Human life is invaluable. When dangerous or life-threatening tasks need to be completed, robotic platforms could be ideal in replacing human operators. Such a task that we focus on in this work is the Explosive Ordnance Disposal. Robot telepresence has the potential to provide safety solutions, given that mobile robots have shown robust capabilities when operating in several environments. However, autonomy may be challenging and risky at this stage, compared to human operation. Teleoperation could be a compromise between full robot autonomy and human presence. In this paper, we present a relatively cheap solution for telepresence and robot teleoperation, to assist with Explosive Ordnance Disposal, using a legged manipulator (i.e., a legged quadruped robot, embedded with a manipulator and RGB-D sensing). We propose a novel system integration for the non-trivial problem of quadruped manipulator whole-body control. Our system is based on a wearable IMU-based motion capture system that is used for teleoperation and a VR headset for visual telepresence. We experimentally validate our method in real-world, for loco-manipulation tasks that require whole-body robot control and visual telepresence

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

With the majority of mobile robot path planning methods being focused on obstacle avoidance, this paper, studies the problem of Navigation Among Movable Obstacles (NAMO) in an unknown environment, with static (i.e., that cannot be moved by a robot) and movable (i.e., that can be moved by a robot) objects. In particular, we focus on a specific instance of the NAMO problem in which the obstacles have to be moved to predefined storage zones. To tackle this problem, we propose an online planning algorithm that allows the robot to reach the desired goal position while detecting movable objects with the objective to push them towards storage zones to shorten the planned path. Moreover, we tackle the challenging problem where an obstacle might block the movability of another one, and thus, a combined displacement plan needs to be applied. To demonstrate the new algorithm's correctness and efficiency, we report experimental results on various challenging path planning scenarios. The presented method has significantly better time performance than the baseline, while also introducing multiple novel functionalities for the NAMO problem