Robot Planning and Control Via Potential Functions

There mingle in the contemporary field of robotics a great many disparate currents of thought from a large variety of disciplines. Nevertheless, a largely unspoken understanding seems to prevail in the field to the effect that certain topics are conceptually distinct. In general, methods of task planning are held to be unrelated to methods of control. The former belong to the realm of geometry and logic whereas the latter inhabit the earthier domain of engineering analysis; geometry is usually associated with off-line computation whereas everyone knows that control must be accomplished in real-time; the one is a “high level” activity whereas the other is at a “low level”. This article concerns one circle of ideas that, in contrast, intrinsically binds action and intention together in the description of the robot’s task. From the perspective of task planning, this point of view seeks to represent abstract goals via a geometric formalism which is guaranteed to furnish a correct control law as well. From the perspective of control theory, the methodology substitutes reference dynamical systems for reference trajectories. From the point of view of computation, less is required off-line, while more is demanded of the real-time controller. From the historical perspective, these techniques represent the effort of engineers to avail themselves of natural physical phenomena in the synthesis of unnatural machines. For more information: Kod*La

Toward a Science of Robot Planning and Control

Programming machines to operate flexibly and autonomously in the physical world seems to require a sophisticated representation that encodes simultaneously the nature of a task, the nature of the environment within which the task is to be performed, and the nature of the robot’s capabilities with respect to both. We seek a scientific methodology of robot task encoding that encompasses the desired behavioral goals and environmental conditions as well. The methodology must balance the need for flexible expression of abstract human goals against the necessity of a eliciting a predictable response from the commanded machine. This talk focuses on the problem of motion planning as an example of how we propose to say what we mean to a robot and to know what we have said. For more information: Kod*La

Experimental Validation of Motion Planning and Control Algorithms on Ground Robot Platforms

The purpose of this research project is to expermentally validate the effectiveness of a robot planning and control algorithm on a ground robot platform

Task Encoding: Toward a Scientific Paradigm for Robot Planning and Control

An autonomous machine requires a description of its designated task in a language that it ‘understands’. The machine language of robots — physical mechanisms endowed with actuators and sensory devices for the purpose of performing work — is the language of dynamical systems. Since the diversity of robotic tasks is immense and our practical experience with computationally well endowed sensed and actuated mechanisms is still very slight, there seems little possibility of imposing at present a meaningful uniformity of description and command methodology on the practice of building these machines. However, a number of thematic desiderata can be articulated whose pursuit will arguably increase the ease of designing robotic algorithms that are predictably successful. This paper presents a summary of our approach to a number of robotic tasks that brings out by example the nature and utility of these desiderata. For more information: Kod*La

Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video: https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page: https://github.com/yuanming-hu/ChainQuee