TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
(2403.07869)Abstract
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole-body teleoperation of mobile manipulators. TeleMoMa unifies multiple human interfaces including RGB and depth cameras, virtual reality controllers, keyboard, joysticks, etc., and any combination thereof. In its more accessible version, TeleMoMa works using simply vision (e.g., an RGB-D camera), lowering the entry bar for humans to provide mobile manipulation demonstrations. We demonstrate the versatility of TeleMoMa by teleoperating several existing mobile manipulators - PAL Tiago++, Toyota HSR, and Fetch - in simulation and the real world. We demonstrate the quality of the demonstrations collected with TeleMoMa by training imitation learning policies for mobile manipulation tasks involving synchronized whole-body motion. Finally, we also show that TeleMoMa's teleoperation channel enables teleoperation on site, looking at the robot, or remote, sending commands and observations through a computer network, and perform user studies to evaluate how easy it is for novice users to learn to collect demonstrations with different combinations of human interfaces enabled by our system. We hope TeleMoMa becomes a helpful tool for the community enabling researchers to collect whole-body mobile manipulation demonstrations. For more information and video results, https://robin-lab.cs.utexas.edu/telemoma-web.
Overview
-
TeleMoMa introduces a modular and versatile system aimed at enhancing mobile manipulation through advanced teleoperation, addressing the critical bottleneck of data acquisition for imitation learning.
-
TeleMoMa integrates various human input interfaces, such as RGB-D cameras, VR controllers, keyboards, and joysticks, offering unprecedented flexibility and accessibility.
-
The system's tripartite architecture allows for adaptability across different robot platforms and scenarios, demonstrated through user studies and the training of imitation learning policies.
-
TeleMoMa's development signifies a leap forward in mobile manipulation research, with implications for both academic explorations and real-world applications.
Unveiling TeleMoMa: A Pathway to Enhanced Imitation Learning through Advanced Teleoperation
Introduction to Teleoperation in Mobile Manipulation
The domain of mobile manipulation, a cornerstone of robotics, aims at expanding the functionality of robots, allowing them to perform a plethora of tasks alongside humans in diverse environments. A pivotal approach in advancing these robots involves learning from human demonstrations, a method that significantly benefits from the application of large-scale datasets. However, a persistent challenge in mobile manipulation is the acquisition of these demonstrations, primarily due to the absence of intuitive and versatile teleoperation systems. Contrary to stationary manipulation, where datasets are plentiful owing to the accessibility of teleoperation frameworks, mobile manipulation tasks demand a sophisticated level of interaction, incorporating mobility and manipulation, often in a bimanual operation context.
The Emergence of TeleMoMa
In this landscape, the introduction of TeleMoMa stands out as a significant contribution. TeleMoMa, short for Teleoperation for Mobile Manipulation, presents itself as a general, modular interface designed to facilitate whole-body teleoperation for mobile manipulators. It seamlessly integrates various human input interfaces, ranging from RGB and depth cameras to virtual reality (VR) controllers, keyboards, and joysticks, thereby offering unprecedented flexibility and accessibility in teleoperation. Notably, TeleMoMa shines in its capacity to operate with just a vision input, such as an RGB-D camera, dramatically lowering the entry barriers for individuals aiming to provide mobile manipulation demonstrations.
Technical Details and Innovations
TeleMoMa's architecture is a tripartite system comprising a Human Interface, a Teleoperation Channel, and a Robot Interface. Its prowess lies in its modular design, allowing for the combination of multiple input devices to suit various teleoperation needs. This design not only makes TeleMoMa adaptable for a wide range of robot platforms but also caters to different teleoperation scenarios, from on-site operating by directly observing the robot to remote operations facilitated through computer networks. In demonstration scenarios involving PAL Tiago++, Toyota HSR, and Fetch robots, TeleMoMa has showcased its versatility and efficacy, thereby underscoring its potential as a vital tool in mobile manipulation research.
Evaluation and Implications
The evaluation of TeleMoMa involves user studies and the training of imitation learning policies for tasks requiring synchronized whole-body motion. These studies validate TeleMoMa's usability and its competency in data collection for imitation learning, subsequently enabling the training of effective policies for complex mobile manipulation tasks. Such advancements are not merely academic; they carry substantial implications for real-world applications, from automating household chores to executing tasks in industrial settings, thereby expanding the horizons of robotic capabilities in human environments.
The Road Ahead
While TeleMoMa heralds a new era in mobile manipulation research by addressing the critical bottleneck of data acquisition, it also opens avenues for future developments. The system's design encourages continuous additions, such as incorporating more input devices for an even broader range of teleoperation scenarios. Moreover, the pursuit of improving teleoperation accuracy, especially in vision-based interfaces, remains a fertile ground for research, promising enhancements in robot learning from human demonstrations.
Conclusion
In conclusion, TeleMoMa emerges as a groundbreaking teleoperation framework, setting a new standard in versatility and modularity for mobile manipulation tasks. By bridging the gap in data collection for imitation learning, it propels the field towards realizing more adept and versatile robots, capable of operating in tandem with humans across myriad settings. As TeleMoMa continues to evolve, it holds the promise of unlocking new possibilities in robotics, making the future of human-robot collaboration more dynamic and productive than ever before.