832 research outputs found
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Recommended from our members
Computer animation for virtual humans.
YesAdvances in computer animation techniques
have spurred increasing levels of
realism and movement in virtual characters that closely
mimic physical reality. Increases in computational
power and control methods enable the creation of 3D
virtual humans for real-time interactive applications.
Artificial intelligence techniques and autonomous
agents give computer-generated characters a life of
their own and let them interact with other characters
in virtual worlds. Developments and advances in networking
and virtual reality (VR) let multiple participants
share virtual worlds and interact with
applications or each other
Motion Synthesis and Control for Autonomous Agents using Generative Models and Reinforcement Learning
Imitating and predicting human motions have wide applications in both graphics and robotics, from developing realistic models of human movement and behavior in immersive virtual worlds and games to improving autonomous navigation for service agents deployed in the real world. Traditional approaches for motion imitation and prediction typically rely on pre-defined rules to model agent behaviors or use reinforcement learning with manually designed reward functions. Despite impressive results, such approaches cannot effectively capture the diversity of motor behaviors and the decision making capabilities of human beings. Furthermore, manually designing a model or reward function to explicitly describe human motion characteristics often involves laborious fine-tuning and repeated experiments, and may suffer from generalization issues. In this thesis, we explore data-driven approaches using generative models and reinforcement learning to study and simulate human motions. Specifically, we begin with motion synthesis and control of physically simulated agents imitating a wide range of human motor skills, and then focus on improving the local navigation decisions of autonomous agents in multi-agent interaction settings. For physics-based agent control, we introduce an imitation learning framework built upon generative adversarial networks and reinforcement learning that enables humanoid agents to learn motor skills from a few examples of human reference motion data. Our approach generates high-fidelity motions and robust controllers without needing to manually design and finetune a reward function, allowing at the same time interactive switching between different controllers based on user input. Based on this framework, we further propose a multi-objective learning scheme for composite and task-driven control of humanoid agents. Our multi-objective learning scheme balances the simultaneous learning of disparate motions from multiple reference sources and multiple goal-directed control objectives in an adaptive way, enabling the training of efficient composite motion controllers. Additionally, we present a general framework for fast and robust learning of motor control skills. Our framework exploits particle filtering to dynamically explore and discretize the high-dimensional action space involved in continuous control tasks, and provides a multi-modal policy as a substitute for the commonly used Gaussian policies. For navigation learning, we leverage human crowd data to train a human-inspired collision avoidance policy by combining knowledge distillation and reinforcement learning. Our approach enables autonomous agents to take human-like actions during goal-directed steering in fully decentralized, multi-agent environments. To inform better control in such environments, we propose SocialVAE, a variational autoencoder based architecture that uses timewise latent variables with socially-aware conditions and a backward posterior approximation to perform agent trajectory prediction. Our approach improves current state-of-the-art performance on trajectory prediction tasks in daily human interaction scenarios and more complex scenes involving interactions between NBA players. We further extend SocialVAE by exploiting semantic maps as context conditions to generate map-compliant trajectory prediction. Our approach processes context conditions and social conditions occurring during agent-agent interactions in an integrated manner through the use of a dual-attention mechanism. We demonstrate the real-time performance of our approach and its ability to provide high-fidelity, multi-modal predictions on various large-scale vehicle trajectory prediction tasks
Large Language Models for Robotics: A Survey
The human ability to learn, generalize, and control complex manipulation
tasks through multi-modality feedback suggests a unique capability, which we
refer to as dexterity intelligence. Understanding and assessing this
intelligence is a complex task. Amidst the swift progress and extensive
proliferation of large language models (LLMs), their applications in the field
of robotics have garnered increasing attention. LLMs possess the ability to
process and generate natural language, facilitating efficient interaction and
collaboration with robots. Researchers and engineers in the field of robotics
have recognized the immense potential of LLMs in enhancing robot intelligence,
human-robot interaction, and autonomy. Therefore, this comprehensive review
aims to summarize the applications of LLMs in robotics, delving into their
impact and contributions to key areas such as robot control, perception,
decision-making, and path planning. We first provide an overview of the
background and development of LLMs for robotics, followed by a description of
the benefits of LLMs for robotics and recent advancements in robotics models
based on LLMs. We then delve into the various techniques used in the model,
including those employed in perception, decision-making, control, and
interaction. Finally, we explore the applications of LLMs in robotics and some
potential challenges they may face in the near future. Embodied intelligence is
the future of intelligent science, and LLMs-based robotics is one of the
promising but challenging paths to achieve this.Comment: Preprint. 4 figures, 3 table
Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks
In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks
Autonomous agents and avatars in REVERIE’s virtual environment
In this paper, we describe the enactment of autonomous agents and avatars in the web-based social collaborative virtual environment of REVERIE that supports natural, human-like behavior, physical interaction and engagement. Represented by avatars, users feel immersed in this virtual world in which they can meet and share experiences as in real life. Like the avatars, autonomous agents that may act in this world are capable of demonstrating human-like non-verbal behavior and facilitate social interaction. We describe how reasoning components of the REVERIE system connect and cooperatively control autonomous agents and avatars representing a user
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