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Robotic systems for long-term monitoring in dense natural environments require a minimal physical footprint with the ability to maneuver through confined spaces, while being robust to harsh conditions. This work presents the design, modeling, and implementation of a compact, tendon driven continuum robot capable of controlled extension and spatial bending within a self-contained, protective actuation package. The system is comprised of a meter-long, single section, three-tendon “tendril” that deploys from a central reel mechanism. Integrated load cells provide tension sensing, and a tip camera supports control feedback and data collection. A Denavit-Hartenberg-based kinematic model is adapted to enable motion planning for the variable-length continuum section. To compensate for modeling inaccuracies arising from the inherent compliance of the flexible tendril, a closed-form tension distribution model is introduced to support feedback based refinement of the robot pose. The robot prototype demonstrated navigating cluttered natural environments and was deployed outdoors over a total of five months, 

This work focuses on online reinforcement learning (RL) in the presence of en- vironmental constraints. Specifically, we consider applications involving robot agents exploring in an environment where obstacles and unsafe zones are present, and the agents must maximize cumulative rewards and at the same time meet the environmental constraints. To address this challenge, we formulate the prob- lem using the constrained Markov Decision Process (CMDP) and incorporate the environmental constraint costs into the policy updates in the proposed Aug- mented Proximal Policy Optimization (APPO) algorithm. At each state and for each possible action, we apply a Variational Auto-Encoder (VAE) [1] to obtain a probabilistic estimate of the discounted cumulative future environmental con- straint costs and integrate them as a regularization term to the reward function. This augmented reward function updates the action-value functions within the APPO algorithm, which is trained by an efficient optimization scheme. Ex- perimental results demonstrate that our methodology enables robot agents to navigate within the safety-constrained regions effectively. 

The paper presents a new Yoshimura origami that exhibits flat foldability. 

Abstract Many protein-protein interactions behave differently in biochemically purified forms as compared to theirin vivostates. As such, determining native protein structures may elucidate structural states previously unknown for even well-characterized proteins. Here we apply the bottom-up structural proteomics method,cryoID, toward a model methanogenic archaeon. While they are keystone organisms in the global carbon cycle and active members of the human microbiome, there is a general lack of characterization of methanogen enzyme structure and function. Through thecryoIDapproach, we successfully reconstructed and identified the nativeMethanosarcina acetivoranspyridoxal 5’-phosphate (PLP) synthase (PdxS) complex directly from cryogenic electron microscopy (cryoEM) images of fractionated cellular lysate. We found that the native PdxS complex exists as a homo-dodecamer of PdxS subunits, and the previously proposed supracomplex containing both the synthase (PdxS) and glutaminase (PdxT) was not observed in cellular lysate. Our structure shows that the native PdxS monomer fashions a single 8α/8β TIM-barrel domain, surrounded by seven additional helices to mediate solvent and interface contacts. A density is present at the active site in the cryoEM map and is interpreted as ribose 5-phosphate. In addition to being the first reconstruction of the PdxS enzyme from a heterogeneous cellular sample, our results reveal a departure from previously published archaeal PdxS crystal structures, lacking the 37 amino acid insertion present in these prior cases. This study demonstrates the potential of applying thecryoIDworkflow to capture native structural states at atomic resolution for archaeal systems, for which traditional biochemical sample preparation is nontrivial.