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Merge branch 'master' of github.com:StanfordASL/robotics_seminar
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‎_talks/cousins_25.md

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speaker: Steve Cousins
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affiliation: Stanford
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website: "[https://profiles.stanford.edu/steve-cousins]"
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website: "https://profiles.stanford.edu/steve-cousins"
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date: 2025-01-10T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB03"

‎_talks/cutkosky_25.md

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speaker: Mark Cutkosky
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affiliation: Stanford University
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website: "http://www-cdr.stanford.edu/~cutkosky/home.html"
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date: 2025-01-31T15:00:00-0000
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location: Gates B03
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location-url: "https://stanford.zoom.us/j/98488529557?pwd=pL6geWgbyA9PXGafgkZ92D9hjLziAl.1"
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title: "ReachBot: Locomotion and Manipulation with Exceptional Reach"
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abstract: "ReachBot is a joint project between Stanford and NASA to explore a new approach to mobility in challenging environments such as martian caves. It consists of a compact robot body with very long extending arms, based on booms used for extendable antennas. The booms unroll from a coil and can extend many meters in low gravity. In rocky environments the booms are equipped with low-mass grippers that use spines for a secure grasp. The booms are strong in tension but vulnerable to buckling in compression or bending. Motion planning with ReachBot therefore has similarities to multifingered grasp planing -- instead of fingers that push, we have booms that pull. Given its very long reach, ReachBot has a large dexterous workspace that simplifies motion planning. However, the sequence of poses must also consider what happens if any grasp fails. In this talk I will introduce the ReachBot design and motion planning considerations, report on a field test with a single ReachBot arm in a lava tube in the Mojave Desert, and discuss future plans, which include the possibility of mounting one or more ReachBot arms equipped with wrists and grippers on a mobile platform – such as ANYMal. To learn more: http://bdml.stanford.edu/ReachBot"
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‎_talks/dixit_25.md

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speaker: Anushri Dixit
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affiliation: UCLA
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website: "[https://www.anushridixit.com/]"
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website: "https://www.anushridixit.com/"
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date: 2025-02-21T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"
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title: "TBD"
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abstract: "TBD"
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title: "Making robots trustworthy: Understanding risk and uncertainty for safe autonomy"
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abstract: "As we deploy robots in increasingly dynamic and unstructured environments with data-driven policies, the need to be able to make guarantees on the reliability and safety of these systems keeps growing. In this talk, I will present two perspectives on uncertainty quantification. First, I will present a conformal prediction-based framework for making in-distribution guarantees on the safety of a learned perception and planning system. Next, I will present a planning framework for out-of-distribution guarantees using coherent risk measures. I will provide the experimental validation of these methods on ground robots for navigation and showcase applications for subterranean search and rescue. Finally, I will present future directions and challenges in attaining reliable autonomy under distribution shifts."
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‎_talks/katzschmann_25.md

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speaker: Robert Katzschmann
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affiliation: ETH Zurich
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website: "[https://srl.ethz.ch/the-group/prof-robert-katzschmann.html]"
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website: "https://srl.ethz.ch/the-group/prof-robert-katzschmann.html"
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date: 2025-01-24T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"

‎_talks/kim_25.md

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speaker: Sangbae Kim
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affiliation: MIT
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website: "[https://meche.mit.edu/people/faculty/[email protected]]"
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website: "https://meche.mit.edu/people/faculty/[email protected]"
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date: 2025-02-07T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"
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title: "TBD"
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abstract: "TBD"
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title: " Physical Intelligence and Cognitive Biases Toward AI"
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abstract: "When will robots be able to clean my house, dishes, and take care of laundry? While we source labor primarily from automated machines in factories, the penetration of physical robots in our daily lives has been slow. What are the challenges in realizing these intelligent machines capable of human level skill? Isn’t AI advanced enough to replace many skills of humans? Unlike conventional robots, which are optimized mainly for position control with almost no adaptability, household tasks require a kind of 'physical intelligence' that involves complex dynamic interactions with overwhelming uncertainties. While advanced language models exemplify AI's prowess in data organization and text generation, a significant divide exists between AI for virtual and physical applications. In this conversation, we'll delve into the cognitive biases that often lead us to underestimate this technological gap. "
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‎_talks/li_25.md

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speaker: Zhuwen Li
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affiliation: Nuro
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website: "https://www.linkedin.com/in/zhuwen-li-59b84a183/"
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date: 2025-03-14T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"
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title: "TBD"
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abstract: "TBD"
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youtube-code: "TBD"
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‎_talks/meier_25.md

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speaker: Franziska Meier
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affiliation: Meta
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website: "[https://fmeier.github.io/]"
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website: "https://fmeier.github.io/"
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date: 2025-02-28T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"

‎_talks/student_win25_1.md

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speaker: Student Speaker - Yifan Hou
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affiliation: Stanford
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website: "https://yifan-hou.github.io/"
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date: 2025-01-17T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB03"
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title: "Active Compliance for Robust Manipulation"
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abstract: "Compliance is a physical property of motion that describes the elastic relationship brings force and motion variations. A suitable compliance profile brings robustness to robotic manipulation by handling uncertainties gracefully. In this talk, I will introduce two sets of methods for designing compliance control in manipulation tasks. I will first walk through manipulation robustness analytically, and show the role compliance control can play to improve it. With basic modeling information, the optimal control/motion plan can be computed efficiently. Then I will talk about how to learn a compliant manipulation policy directly from human demonstrations. We propose Adaptive Compliance Policy (ACP), a framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations."
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# youtube-code: "TBD"
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‎_talks/student_win25_2.md

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speaker: Student Speaker - Daniele Gammelli
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affiliation: Stanford
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website: "https://danielegammelli.github.io/"
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date: 2025-01-17T15:30:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB03"
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title: "Space Autonomy Through the Lens of Foundation Models"
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abstract: "Recent advances across multiple research fields are rapidly changing the way in which we develop autonomous systems. In this talk, I will discuss how space autonomy can benefit from the rise of foundation models. The discussion will focus on two perspectives. First, I will discuss how techniques that are traditional to the foundation model literature can be adapted for the purpose of reliable decision-making in space, with a focus on the application of Transformers for spacecraft trajectory optimization. Next, I will discuss the opportunities presented by pre-trained foundation models within future machine learning-based autonomy stacks for space applications, ranging from data curation to serving as reconfigurable automated reasoning modules within modular autonomy stacks, towards the goal of developing a broadly capable Space Foundation Model."
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# youtube-code: "TBD"
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‎_talks/trimpe_25.md

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speaker: Sebastian Trimpe
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affiliation: MIT
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website: "[https://www.dsme.rwth-aachen.de/cms/DSME/Das-Institut/Team/~jlolt/Prof-Sebastian-Trimpe/?lidx=1]"
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affiliation: RWTH Aachen University
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website: "https://www.dsme.rwth-aachen.de/trimpe"
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date: 2025-02-14T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"
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title: "TBD"
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abstract: "TBD"
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title: "Learning controllers for machines: Paradigms and recent results"
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abstract: "Fast dynamics, nonlinearities, and tedious tuning are just a few of the many reasons why we are interested in leveraging learning for control—challenges that are ubiquitous in robotics and other physical machines. In this talk, we will explore the problem of learning controllers through three paradigms, organized from general to structured learning problems: deep reinforcement learning, automatic imitation learning from optimal control, and auto-tuning via Bayesian optimization. I will highlight some of our recent results addressing key challenges faced in practice, such as enhanced uncertainty quantification for improved data efficiency and reliability in model-based reinforcement learning, as well as parameter-adaptive approximate model predictive control for imitation learning without retraining. By discussing these advancements alongside applications—demonstrated through hardware experiments on unicycle robots, quadcopters, and cars—I aim to develop an understanding of the potential of these paradigms in both research and current practice."
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‎_talks/yao_25.md

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speaker: Lining Yao
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affiliation: UC Berkeley
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website: "[https://me.berkeley.edu/people/lining-yao/]"
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website: "https://me.berkeley.edu/people/lining-yao/"
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date: 2025-03-07T15:00:00-0000
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location: Gates B03
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location-url: "https://campus-map.stanford.edu/?srch=GatesB01"

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