31 accepted papers
We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reach-avoid problems</i>. These problems require computing the <italic xmlns:mml="http://www.w3.org/1
Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic w
Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the environment. A challenge, however, is that no model is perfect; moreover, we often do not know wh…
Contact-rich robotic systems, such as legged robots and manipulators, are often represented as hybrid systems. However, the stability analysis and region-of-attraction computation for these systems are often challenging because of the discontinuous state changes upon contact (also referred to as <it
When a robot observes another agent unexpectedly modifying their behavior, inferring the most likely cause is a valuable tool for maintaining safety and reacting appropriately. In this work, we present a novel method for inferring constraints that works on continuous, possibly sub-optimal demonstrat…
Designing human motion predictors which preserve safety while maintaining robot efficiency is an increasingly important challenge for robots operating in close physical proximity to people. One approach is to use robust control predictors that safeguard against every possible future human state, lea
Predictive human models often need to adapt their parameters online from human data. This raises previously ignored safety-related questions for robots relying on these models such as what the model could learn online and how quickly could it learn it. For instance, when will the robot have a confid…
In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games. These games are often used to formulate both collision avoidance and goal satisfaction. Our method is computationally efficient, running in real-time for scenarios involving multiple pl…
Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical control systems. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the abilit…
Robotic systems frequently operate under changing dynamics, such as driving across varying terrain, encountering sensing and actuation faults, or navigating around humans with uncertain and changing intent. In order to operate effectively in these situations, robots must be capable of efficiently es
Robots deployed in real-world environments should operate safely in a robust manner. In scenarios where an "ego" agent navigates in an environment with multiple other "non-ego" agents, two modes of safety are commonly proposed—adversarial robustness and probabilistic constraint satisfaction. However…
Autonomous systems like aircraft and assistive robots often operate in scenarios where guaranteeing safety is critical. Methods like Hamilton-Jacobi reachability can provide guaranteed safe sets and controllers for such systems. However, often these same scenarios have unknown or uncertain environme…
Real world visual navigation requires robots to operate in unfamiliar, human-occupied dynamic environments. Navigation around humans is especially difficult because it requires anticipating their future motion, which can be quite challenging. We propose an approach that combines learning-based perce
Real-world autonomous systems often employ probabilistic predictive models of human behavior during planning to reason about their future motion. Since accurately modeling human behavior a priori is challenging, such models are often parameterized, enabling the robot to adapt predictions based on ob…
We design an insect-sized rolling microrobot driven by continuously rotating wheels. It measures 18 mm × 8 mm × 8 mm. There are 2 versions of the robot - a 96 mg laser-powered one and a 130 mg supercapacitor powered one. The robot can move at 27 mm/s (1.5 body lengths per second) with wheels rotatin
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multi-player general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning pro…
Here we present the design of an insect-scale electromagnetic actuator that can be used to spin wings and generate lift. This lift generation method is in contrast to most other microrobot designs at this size scale which rely on flapping wings to produce lift. The device has a wing span of 4 centim
Many problems in robotics involve multiple decision making agents. To operate efficiently in such settings, a robot must reason about the impact of its decisions on the behavior of other agents. Differential games offer an expressive theoretical framework for formulating these types of multi-agent p…
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dyna…
Hamilton-Jacobi (HJ) reachability analysis has been developed over the past decades into a widely-applicable tool for determining goal satisfaction and safety verification in nonlinear systems. While HJ reachability can be formulated very generally, computational complexity can be a serious impedime…
Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamic…
Safety analysis is a necessary component in the design and deployment of autonomous robotic systems. Techniques from robust optimal control theory, such as Hamilton-Jacobi reachability analysis, allow a rigorous formalization of safety as guaranteed constraint satisfaction. Unfortunately, the comput…
Parametric dynamic movement primitives (PDMPs) are powerful motion representation algorithms, which encode multiple demonstrations and generalize them. As an online trajectory from PDMPs emulates the provided demonstrations, managing the safety guarantee of the demonstrations for a given scenario is
Hamilton-Jacobi (HJ) reachability provides a flexible framework for the verification of safety in robotic systems: it accounts for nonlinear system dynamics and provides safety-preserving controllers. However, computational scalability limits its direct application to systems of less than five conti…
Ensuring safety in trajectory planning of multirotor systems is an essential element for risk-free operation. Even if the generated trajectory is known to be safe in the planning phase, unknown disturbance during an actual operation can lead to a dangerous situation. This paper proposes safety-guara…
We present a new framework for motion planning that wraps around existing kinodynamic planners and guarantees recursive feasibility when operating in a priori unknown, static environments. Our approach makes strong guarantees about overall safety and collision avoidance by utilizing a robust control…
We present a 70mg, 3cm wing-span, flapping wing aerial vehicle capable of generating up to 60mg of lift using an electromagnetic actuator with low-voltage input (≈5.5V). Its design is novel, with the actuation and transmission integrated into a single resonant mechanism, thus not requiring any small…
Each person has a different personal space and behaves differently when another person approaches. Based on this observation, we propose a novel method to learn how to approach a person comfortably based on the person's preference while avoiding uncomfortable encounters. We propose a personal comfor
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost. This work builds on a recen…
This letter presents a path planning algorithm for generating a cost-efficient path that satisfies mission requirements specified in linear temporal logic (LTL). We assume that a cost function is defined over the configuration space. Examples of a cost function include hazard levels, wireless connec
Hamilton-Jacobi (HJ) reachability is a method that provides rigorous analyses of the safety properties of dynamical systems. These guarantees can be provided by the computation of a backward reachable set (BRS), which represents the set of states from which the system may be driven into violating sa…