Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. But most importantly, our algorithm achieves a worst-case speed-up of 160x over the latter approach.
We present an orientation adaptive controller to compensate for the effects of highly constrained environments on continuum manipulator actuation. A transformation matrix updated using optimal estimation techniques from optical flow measurements captured by the distal camera is composed with any Jacobian estimation or kinematic model to compensate for these effects. By utilizing domain knowledge to define the structure of this matrix, fewer parameters need to be estimated and a stable controller can be guaranteed. The algorithm is tested on a custom robotic catheter and convergence is shown bo…
Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators
Robotics Research Center, IIIT Hyderabad, India（IIIT Hyderabad机器人研究中心，印度）；Department of Computer Science, University of Manchester, UK（曼彻斯特大学计算机科学系，英国）；Newcastle University, UK（新castle大学，英国）
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
Department of Computer Science, The University of Manchaster（曼彻斯特大学计算机科学系）；International Institute of Information Technology Hyderabad（海得拉巴国际信息科技研究所）；Department of Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人系）；Newcastle University（新castle大学）
Impedance and Stability Targeted Adaptation for Aerial Manipulator with Unknown Coupling Dynamics
Robotics Research Center, International Institute of Information Technology Hyderabad (IIIT-H)（印度海得拉巴国际信息技术学院机器人研究中心）；The University of Manchester（曼彻斯特大学）；Southeast University（东南大学）
University of Oslo（奥斯陆大学）；Oslo University Hospital（奥斯陆大学医院）
With advancing technologies, robotic manipulators and visual environment sensors are becoming cheaper and more widespread. However, robot control can be still a limiting factor for better adaptation of these technologies. Robotic manipulators are performing very well in structured workspaces, but do not adapt well to unexpected changes, like people entering the workspace. We present a method combining 3D Camera based workspace mapping, and a predictive and reflexive robot manipulator trajectory estimation to allow more efficient and safer operation in dynamic workspaces. In experiments on a re…
Tele-rehabilitation with online skill transfer and adaptation in $\mathbb{R}^3 \times \mathit{S}^3$
MIRMI - Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Germany（慕尼黑工业大学机器人与机器智能研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Visual Intelligence Lab, Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology（香港科技大学成卡顺机器人研究所视觉智能实验室）；Differential Robotics Technology Company, Ltd.（差分机器人技术有限公司）
Sim-to-Real Transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-Shot Deployment on Robotic Manipulators through Visual Domain Adaptation
Institute for Research in Technology (IIT)（技术研究 institute）；ICAI School of Engineering（工程学院）；Comillas Pontifical University（宗座大学）
Maintaining safety under adaptation has long been considered to be an important capability for autonomous systems. As these systems estimate and change the ego-model of the system dynamics, questions regarding how to develop safety guarantees for such systems continue to be of interest. We propose a novel robust safe control methodology that uses set-based safety constraints to make a robotic system with dynamical uncertainties safely adapt and operate in its environment. The method consists of designing a scalar energy function (safety index) for an adaptive system with parametric uncertainty…
Incremental Language Understanding for Online Motion Planning of Robot Manipulators
Human-Robot Interaction Lab, Tufts University（塔夫茨大学人机交互实验室）；Automation and Control Institute (ACIN), TU Wien（自动化与控制研究所（ACIN））；center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究院）
The University of Tokyo（东京大学）；Matsuo Institute（松尾研究所）
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur d…
X, The Moonshot Factory（X，登月工厂）；UC Berkeley（加州大学伯克利分校）；Google（谷歌）
Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are expensive both because they require large amounts of real world training demonstrations and because identifying the best model to deploy in the real world requires time-consuming real-world evaluations. These challenges can be mitigated by simulation: by supplementing real world data with simulated demonstrations and using simulated evaluations to identify high performing policies. However, this introduces the well-known "reality gap" problem, where …
