publications and other writing

2023

Closing the Loop on Runtime Monitors with Fallback-Safe MPC

R. Sinha, E. Schmerling, and M. Pavone

In Proc. IEEE Conf. on Decision and Control, 2023

(in press)

Abs arXiv Website

When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system’s safety. This raises fundamental questions on how we can assess confidence in perception systems and to what extent we can take safety-preserving actions when external environmental changes degrade our perception model’s performance. Therefore, we present a framework to certify the safety of a perception-enabled system deployed in novel contexts. To do so, we leverage robust model predictive control (MPC) to control the system using the perception estimates while maintaining the feasibility of a safety-preserving fallback plan that does not rely on the perception system. In addition, we calibrate a runtime monitor using recently proposed conformal prediction techniques to certifiably detect when the perception system degrades beyond the tolerance of the MPC controller, resulting in an end-to-end safety assurance. We show that this control framework and calibration technique allows us to certify the system’s safety with orders of magnitudes fewer samples than required to retrain the perception network when we deploy in a novel context on a photo-realistic aircraft taxiing simulator. Furthermore, we illustrate the safety-preserving behavior of the MPC on simulated examples of a quadrotor. We open-source our simulation platform and provide videos of our results at our project page: \urlhttps://tinyurl.com/fallback-safe-mpc.
Semantic Anomaly Detection with Large Language Models

A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D Nesnas, and M. Pavone

Autonomous Robots, 2023

Special Issue on Large Language Models in Robotics (in press)

Abs arXiv

As robots acquire increasingly sophisticated skills and see increasingly complex and varied environments, the threat of an edge case or anomalous failure is ever present. For example, Tesla cars have seen interesting failure modes ranging from autopilot disengagements due to inactive traffic lights carried by trucks to phantom braking caused by images of stop signs on roadside billboards. These system-level failures are not due to failures of any individual component of the autonomy stack but rather system-level deficiencies in semantic reasoning. Such edge cases, which we call \textitsemantic anomalies, are simple for a human to disentangle yet require insightful reasoning. To this end, we study the application of large language models (LLMs), endowed with broad contextual understanding and reasoning capabilities, to recognize these edge semantic cases. We introduce a monitoring framework for semantic anomaly detection in vision-based policies to do so. Our experiments evaluate this framework in monitoring a learned policy for object manipulation and a finite state machine policy for autonomous driving and demonstrate that an LLM-based monitor can serve as a proxy for human reasoning. Finally, we provide an extended discussion on the strengths and weaknesses of this approach and motivate a research outlook on how we can further use foundation models for semantic anomaly detection.
Semantic Anomaly Detection with Large Language Models

A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D Nesnas, and M. Pavone

In Robotics, Systems and Science; Workshop Towards Safe Autonomy: New Challenges and Trends in Robot Perception, 2023

arXiv
Online Distribution Shift Detection via Recency Prediction

R. Luo, R. Sinha, A. Hindy, S. Zhao, S. Savarese, E. Schmerling, and M. Pavone

arxiv preprint, arxiv:2211.09916, 2023

Abs arXiv

When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical. However, most existing methods for detecting distribution shift are not well-suited to robotics settings, where data often arrives in a streaming fashion and may be very high-dimensional. In this work, we present an online method for detecting distribution shift with guarantees on the false positive rate - i.e., when there is no distribution shift, our system is very unlikely (with probability <ϵ) to falsely issue an alert; any alerts that are issued should therefore be heeded. Our method is specifically designed for efficient detection even with high dimensional data, and it empirically achieves up to 11x faster detection on realistic robotics settings compared to prior work while maintaining a low false negative rate in practice (whenever there is a distribution shift in our experiments, our method indeed emits an alert).

2022

A System-Level View on Out-of-Distribution Data in Robotics

R. Sinha, S. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone

arXiv preprint arXiv:2212.14020, 2022

Abs arXiv

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall \textitsystem-level competence of a robot as it operates in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty

R. Sinha, J. Harrison, S. M. Richards, and M. Pavone

In American Control Conference, 2022

Abs arXiv Website

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws pioneered in classical adaptive control to achieve significant performance improvements in the presence of uncertainties of large magnitude, a setting in which existing learning-based predictive control algorithms often struggle to guarantee safety. In contrast to previous work in robust adaptive MPC, our approach allows us to take advantage of structure (i.e., the numerical predictions) in the a priori unknown dynamics learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when we cannot directly cancel the additive uncertain function from the dynamics. Moreover, we apply contemporary statistical estimation techniques to certify the system’s safety through persistent constraint satisfaction with high probability. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods.
Adaptive Robust Model Predictive Control via Uncertainty Cancellation

R. Sinha, J. Harrison, S. M. Richards, and M. Pavone

IEEE Transactions on Automatic Control, 2022

(under review).

Abs arXiv

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws pioneered in classical adaptive control to achieve significant performance improvements in the presence of uncertainties of large magnitude, a setting in which existing learning-based predictive control algorithms often struggle to guarantee safety. In contrast to previous work in robust adaptive MPC, our approach allows us to take advantage of structure (i.e., the numerical predictions) in the a priori unknown dynamics learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when we cannot directly cancel the additive uncertain function from the dynamics. We apply contemporary statistical estimation techniques to certify the system’s safety through persistent constraint satisfaction with high probability. Moreover, we propose using Bayesian meta-learning algorithms that learn calibrated model priors to help satisfy the assumptions of the control design in challenging settings. Finally, we show in simulation that our method can accommodate more significant unknown dynamics terms than existing methods and that the use of Bayesian meta-learning allows us to adapt to the test environments more rapidly.
Cautious Markov Games for Interaction Aware Robotics

R. Sinha, and S. Lall

In Conference on Robot Learning: Workshop on Strategic Multi-Agent Interactions, 2022

Abs Website

Autonomous vehicles (AVs) and other agents typically interact in structured environments with rules and conventions that all agents \emphshould follow, but do not always do, such as in traffic. Therefore, we study 1) how to incorporate these rules and conventions into multi-agent robotics problems and 2) what the implications are for interaction-aware methods for decision making. To do this, we express the rules as linear temporal logical constraints on the joint state trajectory and model the multi-agent interaction as a stochastic game. We learn the likelihood of other agents making decisions that can violate the rules as chance constraints to interpretably represent the tendency of agents to break the rules, rather than implicitly encode it in the agents’ preference structure. We dub this framework the cautious Markov game (CMG), for which we efficiently construct policies using robust dynamic programming. We find that we can significantly reduce the conservatism of robust policies by exploiting the rule-based nature of the game on illustrative examples, thereby confirming our intuition that traffic rules significantly reduce the need for inter-agent negotiation.

2021

Covariate Shifts in Multi-Agent Interactions

R. Brown*, R. Dyro*, and R. Sinha*

Tech. Report for Stanford CS329D, 2021

PDF
Solving Multi-Agent Zero-Sum Games with Mirror Descent

R. Sinha

Tech. Report for EE364B, 2021

PDF
Pose Graph Optimization Using Matrix Sketching

R. Sinha*, and E. Soroka*

Tech. Report for AA273, 2021

PDF
Multi-Vehicle Autonomous Racing with Learning MPC and Trajectory Forecasting

R. Sinha

Tech. Report for AA277, 2021

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2020

Cautious Markov Games, a Novel Framework for Human-Robot Interaction

R. Sinha

Tech. Report for AA228, 2020

(Best Project Paper)

2019

Data-Poisoning for Linear Models

A. Narang*, R. Sinha*, A. Siththaranjan*, and F. Yang*

Tech. Report for UC Berkeley EE227B, 2019

PDF
Oversized Load Lifting and Yielding (Project OLLY)

J. Anderson*, B. Chang*, R. Cosner*, S. Kruger*, R. Lim*, and R. Sinha*

Tech. Report for UC Berkeley ME102B, 2019

(Best Project)

PDF Poster
MPC Control of Multiple Quadcopters Cooperatively Lifting an Object

R. Anand*, J. Anderson*, R. Lim*, and R. Sinha*

Tech. Report for UC Berkeley ME231A, 2019

PDF

2017

Goldeneye AB1

R. Anand*, A. English*, D. Gao*, S. Malekshahi*, R. Sinha*, and N. Stevenson*

In Presented at NASA Aeronautics Design Challenge, 2017

(Third place/honorable mention)

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