mit-6
http://dspace.mit.edu:80
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.2022-01-25T03:38:49ZNeural Stochastic Contraction Metrics for Learning-based Control and Estimation
https://hdl.handle.net/1721.1/139679
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
Tsukamoto, Hiroyasu; Chung, Soon-Jo; Slotine, Jean-Jacques E
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable learning-based control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural network to construct a contraction metric and its differential Lyapunov function, sampled via simplified convex optimization in the stochastic setting. Spectral normalization constrains the state-derivatives of the metric to be Lipschitz continuous, thereby ensuring exponential boundedness of the mean squared distance of system trajectories under stochastic disturbances. The trained NSCM model allows autonomous systems to approximate optimal stable control and estimation policies in real-time, and outperforms existing nonlinear control and estimation techniques including the state-dependent Riccati equation, iterative LQR, EKF, and the deterministic NCM, as shown in simulation results.
2021-01-01T00:00:00ZImage interpretation by iterative bottom-up top-down processing
https://hdl.handle.net/1721.1/139678
Image interpretation by iterative bottom-up top-down processing
Ullman, Shimon; Assif, Liav; Strugatski, Alona; Vatashsky, Ben-Zion; Levi, Hila; Netanyahu, Aviv; Yaari, Adam
Scene understanding requires the extraction and representation of scene components, such as objects and their parts, people, and places, together with their individual properties, as well as relations and interactions between them. We describe a model in which meaningful scene structures are extracted from the image by an iterative process, combining bottom-up (BU) and top-down (TD) networks, interacting through a symmetric bi-directional communication between them (‘counter-streams’ structure). The BU- TD model extracts and recognizes scene constituents with their selected properties and relations, and uses them to describe and understand the image.
The scene representation is constructed by the iterative use of three components. The first model component is a bottom-up stream that extracts selected scene elements, properties and relations. The second component (‘cognitive augmentation’) augments the extracted visual representation based on relevant non-visual stored representations. It also provides input to the third component, the top-down stream, in the form of a TD instruction, instructing the model what task to perform next. The top-down stream then guides the BU visual stream to perform the selected task in the next cycle. During this
process, the visual representations extracted from the image can be combined with relevant non- visual representations, so that the final scene representation is based on both visual information extracted from the scene and relevant stored knowledge of the world.
We show how the BU-TD model composes complex visual tasks from sequences of steps, invoked by individual TD instructions. In particular, we describe how a sequence of TD-instructions is used to extract from the scene structures of interest, including an algorithm to automatically select the next TD- instruction in the sequence. The selection of TD instruction depends in general on the goal, the image, and on information already extracted from the image in previous steps. The TD-instructions sequence is therefore not a fixed sequence determined at the start, but an evolving program (or ‘visual routine’) that depends on the goal and the image.
The extraction process is shown to have favourable properties in terms of combinatorial generalization,
generalizing well to novel scene structures and new combinations of objects, properties and relations not seen during training. Finally, we compare the model with relevant aspects of the human vision, and suggest directions for using the BU-TD scheme for integrating visual and cognitive components in the process of scene understanding.
2021-11-01T00:00:00ZImplicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction
https://hdl.handle.net/1721.1/139677
Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction
Boffi, Nicholas M; Slotine, Jean-Jacques E
Stable concurrent learning and control of dynamical systems is the subject of adaptive control. Despite being an established field with many practical applications and a rich theory, much of the development in adaptive control for nonlinear systems revolves around a few key algorithms. By exploiting strong connections between classical adaptive nonlinear control techniques and recent progress in optimization and machine learning, we show that there exists considerable untapped potential in algorithm development for both adaptive nonlinear control and adaptive dynamics prediction. We begin by introducing first-order adaptation laws inspired by natural gradient descent and mirror descent. We prove that when there are multiple dynamics consistent with the data, these non-Euclidean adaptation laws implicitly regularize the learned model. Local geometry imposed during learning thus may be used to select parameter vectors—out of the many that will achieve perfect tracking or prediction—for desired properties such as sparsity. We apply this result to regularized dynamics predictor and observer design, and as concrete examples, we consider Hamiltonian systems, Lagrangian systems, and recurrent neural networks. We subsequently develop a variational formalism based on the Bregman Lagrangian. We show that its Euler Lagrange equations lead to natural gradient and mirror descent-like adaptation laws with momentum, and we recover their first-order analogues in the infinite friction limit. We illustrate our analyses with simulations demonstrating our theoretical results.
2021-01-01T00:00:00ZDecentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies
https://hdl.handle.net/1721.1/139676
Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies
Culbertson, Preston; Slotine, Jean-Jacques; Schwager, Mac
In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in SE(3) . The robots do not know the mass or friction properties of the object, or where they are attached to the object. They can, however, access a common state measurement, either from one robot broadcasting its measurements to the team, or by all robots communicating and averaging their state measurements to estimate the state of their centroid. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller’s behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller’s performance using numerical simulations of a manipulation task in 3-D, as well as hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodeled effects, such as discretization errors and complex frictional interactions.
2021-01-01T00:00:00Z