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There is a lack of cleaning robots dedicated to the scrubbing of contaminated surfaces. Contaminated surfaces in domestic and industrial settings typically require manual scrubbing which can be costly or hazardous. There is growing demand for automated sanitization systems in hospitals, foodprocessing plants, and other settings where cleanliness of surfaces is important. To address the opportunity to automate the scrubbing of surfaces, this work focuses on the use of series elastic actuators which can apply consistent trajectories of scrubbing force. Consistent force during scrubbing increases the rate of removal for a contaminant. An elastic robot which has rigid links and low-stiffness joints can perform friction-based cleaning of surfaces with complex geometries while maintaining consistent scrubbing force. This study uses a hybrid forceposition control scheme and a low-cost elastic robot to perform scrubbing. This study observes the relationship between joint stiffness in the robot and the disturbance rejection for force-based control during scrubbing. 

Understanding pedestrian behavior patterns is key for building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are often impractical since pedestrian behaviors may be unknown a priori and manually generating behavior labels is prohibitively time consuming. We utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. Then, we show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing distributions of behaviors in a space. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results outperforming SOTA on the ETH and UCY datasets. 

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a ``region graph'', which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFSDP performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives. 

The Covid-19 pandemic is a reminder that modern society is still susceptible to multiple types of natural or man-made disasters, which motivates the need to improve resiliency through technological advancement. This article focuses on robotics and the role it can play towards providing resiliency to disasters. The progress in this domain brings the promise of effectively deploying robots in response to life-threatening disasters, which includes highly unstructured setups and hazardous spaces inaccessible or harmful to humans. This article discusses the maturity of robotics technology and explores the needed advances that will allow robots to become more capable and robust in disaster response measures. It also explores how robots can help in making human and natural environments preemptively more resilient without compromising long-term prospects for economic development. Despite its promise, there are also concerns that arise from the deployment of robots. Those discussed relate to safety considerations, privacy infringement, cyber-security, and financial aspects, such as the cost of development and maintenance as well as impact on employment.