Patrick Slade

Raymond Liu, Patrick Slade

Globally, 340 million people have blindness or moderate to severe visual impairment (BVI)$^1$ which limits independent outdoor navigation$^2$ and negatively affects their health and quality of life$^{3,4}$. We surveyed 112 people with BVI and found that an ideal outdoor navigation aid must be able to perform turn-by-turn directions, path guidance, and obstacle detection and avoidance. Existing navigation tools such as white canes, guide dogs, and electronic travel aids often lack one or more of these criteria and may be expensive or inaccessible$^{5,6}$. Here we introduce Mobilio, a smartphone application that incorporates machine learning, sensor fusion algorithms, and personalized audio feedback to meet all of the outdoor navigation criteria. The reliability of the smartphone sensors and models used for navigation were assessed with engineering tests in representative navigation scenarios. We performed a series of experiments where Mobilio personalized audio feedback for participants with BVI (n = 14), guided them along an outdoor community path, and helped them navigate an obstacle course. Participants walking with Mobilio and a white cane reduced time to navigate a community path by 13 $\pm$ 3% and environmental contacts by 41 $\pm$ 5% compared to using Google Maps and a white cane. Mobilio achieved similar outdoor navigation reliability as a human guide. Participant surveys reported that Mobilio was easy to use, had a low perceived workload, and provided intuitive audio feedback. This work provides an accessible and personalized tool that may be an effective outdoor navigation aid to increase independence for people with BVI.

Patrick Slade, Zachary N. Sunberg, Mykel J. Kochenderfer

Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modeling errors. However, information gathering actions often conflict with optimal actions for reaching control objectives, requiring a trade-off between exploration and exploitation. The specific problem setting considered here is for discrete-time nonlinear systems, with process noise, input-constraints, and parameter uncertainty. This article frames this problem as a Bayes-adaptive Markov decision process and solves it online using Monte Carlo tree search with an unscented Kalman filter to account for process noise and parameter uncertainty. This method is compared with certainty equivalent model predictive control and a tree search method that approximates the QMDP solution, providing insight into when information gathering is useful. Discrete time simulations characterize performance over a range of process noise and bounds on unknown parameters. An offline optimization method is used to select the Monte Carlo tree search parameters without hand-tuning. In lieu of recursive feasibility guarantees, a probabilistic bounding heuristic is offered that increases the probability of keeping the state within a desired region.

Patrick Slade, Siobhan Powell, Michael F. Howland

The benefits of legged locomotion shown in nature overcome challenges such as obstacles or terrain smoothness typically encountered with wheeled vehicles. This paper evaluates the benefits of using optimal control on a single leg hopper during the entire hopping motion. Basic control without considering physical constraints is implemented through hand-tuned PD controllers following the Raibert control framework. The differential flatness of the first-order equations of motion and the Liouvillian property for the second-order equations for the hopper system are proved, enabling flat outputs for control. A two-point boundary value problem (BVP) is then used to minimize jerk in the flat system to gain implicit smoothness in the output controls. This smoothness ensures that the planned trajectories are feasible, allowing for given waypoints to be reached.

Nathan Kau, Aaron Schultz, Natalie Ferrante et al.

This paper presents Stanford Doggo, a quasi-direct-drive quadruped capable of dynamic locomotion. This robot matches or exceeds common performance metrics of state-of-the-art legged robots. In terms of vertical jumping agility, a measure of average vertical speed, Stanford Doggo matches the best performing animal and surpasses the previous best robot by 22%. An overall design architecture is presented with focus on our quasi-direct-drive design methodology. The hardware and software to replicate this robot is open-source, requires only hand tools for manufacturing and assembly, and costs less than $3000.

Patrick Slade, Preston Culbertson, Zachary Sunberg et al.

Robots performing manipulation tasks must operate under uncertainty about both their pose and the dynamics of the system. In order to remain robust to modeling error and shifts in payload dynamics, agents must simultaneously perform estimation and control tasks. However, the optimal estimation actions are often not the optimal actions for accomplishing the control tasks, and thus agents trade between exploration and exploitation. This work frames the problem as a Bayes-adaptive Markov decision process and solves it online using Monte Carlo tree search and an extended Kalman filter to handle Gaussian process noise and parameter uncertainty in a continuous space. MCTS selects control actions to reduce model uncertainty and reach the goal state nearly optimally. Certainty equivalent model predictive control is used as a benchmark to compare performance in simulations with varying process noise and parameter uncertainty.