Achieving agile, efficient, and robust locomotion in bipedal robots remains a grand challenge of robotics. Traditional model-based control methods are theoretically grounded but are often sensitive to model mismatch and state-estimation uncertainty, limiting their adaptability to real-world environments. Conversely, data-driven approaches such as reinforcement learning produce remarkable behaviors but often lack interpretability, require non-trivial reward shaping, and raise safety concerns.
\r\n\r\nThis thesis bridges these two paradigms through a unified framework that begins with model-based behavior synthesis and culminates in data-driven adaptation. The first part focuses on constructing walking behaviors and controllers using reduced-order models of locomotion. A hierarchy of planners and controllers is developed to enable robust walking for flat-footed and multi-domain gaits, as well as safety-critical locomotion over constrained footholds such as stairs and stepping stones. Additionally, this work introduces RoMoCo, a modular open-source architecture, a modular open-source architecture designed to unify reduced-order planning, output synthesis, and whole-body control across multiple bipedal platforms.
\r\n\r\nBuilding on this foundation, the second part introduces data-driven mechanisms that enable robots to improve and personalize their behaviors through various forms of data. Episodic data collected during repeated executions are used to correct modeling errors and reduce constraint violations. Human preference data facilitates automatic gain tuning through interactive feedback. Online robot data enables adaptation of reduced-order models by learning step-to-step dynamics directly from real executions. Finally, large-scale simulation data support a reinforcement-learning framework designed for hardware deployment, where model-guided rewards enable efficient training and introduce perception inputs, yielding policies capable of dynamic stepping-stone traversal on real robots.
\r\n\r\nTogether, these contributions form a progression from theoretically grounded model-based control to data-enabled adaptation, demonstrating that reduced-order models and data-driven learning are complementary. Their integration enables bipedal robots such as Cassie and G1 to walk safely, robustly, and efficiently across diverse terrains, marking a step toward human-level agility in legged locomotion.
", "doi": "10.7907/e1sk-7771", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:17394", "collection": "thesis", "collection_id": "17394", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06032025-023615578", "primary_object_url": { "basename": "Noel_Csomay_Shanklin_Thesis.pdf", "content": "final", "filesize": 134391515, "license": "other", "mime_type": "application/pdf", "url": "/17394/1/Noel_Csomay_Shanklin_Thesis.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Layered Control Architectures: Constructive Theory and Application to Legged Robots", "author": [ { "family_name": "Csomay-Shanklin", "given_name": "Noel V.", "orcid": "0000-0002-2361-1694", "clpid": "Csomay-Shanklin-Noel-V" } ], "thesis_advisor": [ { "family_name": "Ames", "given_name": "Aaron D.", "orcid": "0000-0003-0848-3177", "clpid": "Ames-A-D" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "orcid": "0000-0002-3091-540X", "clpid": "Burdick-J-W" }, { "family_name": "Ames", "given_name": "Aaron D.", "orcid": "0000-0003-0848-3177", "clpid": "Ames-A-D" }, { "family_name": "Yue", "given_name": "Yisong", "orcid": "0000-0001-9127-1989", "clpid": "Yue-Yisong" }, { "family_name": "Niemeyer", "given_name": "Gunter", "clpid": "Niemeyer-Gunter" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Fueled in part by the imagination of science fiction, every decade since the 1950s has expected robots to enter our everyday lives in the subsequent decade. Despite this anticipation, the widespread adoption of robots has consistently fallen short of societal expectations. This delay is attributable to the sheer variety of complexities in robotics --- perception, contact-rich dynamics, human-robot interactions. Each sub-discipline of robotics poses unique challenges that must be addressed to achieve general autonomy. As progress is made in these sub-fields, it is increasingly important to adopt a layered architecture perspective that combines isolated controller blocks into a unified framework.
\r\n\r\nThis thesis argues that on the road to general autonomy, adopting layered architectures enables three key benefits: efficiency, feasibility, and generalizability. We root our discussion in a general problem in robotics: the design of a controller that navigates a robot to a goal state while satisfying all state and input constraints that are present. Throughout the thesis, we focus on solutions that are both general --- applicable across a wide variety of robotic platforms --- and concrete --- deployed and tested on specific hardware platforms. As such, we aim to not only propose a framework for reasoning about this problem, but also methods to synthesize controllers that solve it in practice for legged robots.
\r\n\r\nWe begin by motivating and formalizing the notion of layered architectures and use this to build our control stack from the bottom up. We start with low-level planning and tracking layers that stabilize the system within a tracking tube for both the actuated and underactuated states of legged robots. We then introduce high-level planning and tracking layers that generate and follow sparse, dynamically feasible graphs for coarse global navigation through cluttered environments. By decomposing the global control problem into interacting levels and layers, each operating with disparate timescales and system abstractions, we enable tractable, reliable, and extensible robot autonomy.
\r\n\r\nThroughout this thesis, an emphasis will be placed on mathematical structure, constructive synthesis, and experimental validation. We demonstrate that adopting a layered architecture perspective is not merely an implementation convenience, but a fundamental organizing principle that can enable true robot autonomy.
", "doi": "10.7907/k0ns-c606", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:17351", "collection": "thesis", "collection_id": "17351", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06022025-015834886", "primary_object_url": { "basename": "RKC_Thesis-7-compressed.pdf", "content": "final", "filesize": 19037198, "license": "other", "mime_type": "application/pdf", "url": "/17351/1/RKC_Thesis-7-compressed.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Dynamic Safety Under Uncertainty: A Control Barrier Function Approach", "author": [ { "family_name": "Cosner", "given_name": "Ryan Kazuo", "orcid": "0000-0002-4035-1425", "clpid": "Cosner-Ryan-Kazuo" } ], "thesis_advisor": [ { "family_name": "Ames", "given_name": "Aaron D.", "orcid": "0000-0003-0848-3177", "clpid": "Ames-A-D" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "orcid": "0000-0002-3091-540X", "clpid": "Burdick-J-W" }, { "family_name": "Yue", "given_name": "Yisong", "orcid": "0000-0001-9127-1989", "clpid": "Yue-Yisong" }, { "family_name": "Niemeyer", "given_name": "Gunter", "clpid": "Niemeyer-Gunter" }, { "family_name": "Culbertson", "given_name": "Preston", "orcid": "0000-0002-1403-8697", "clpid": "Culbterson-Preston" }, { "family_name": "Ames", "given_name": "Aaron D.", "orcid": "0000-0003-0848-3177", "clpid": "Ames-A-D" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Modern technological achievements in robotics, machine learning, and control promise an exciting future where autonomous robots are a useful part of everyday life, from automated manufacturing and driverless cars to robotic healthcare and autonomous delivery drones. However, as robots are deployed in increasingly complex, uncertain, and human-interactive environments, safety becomes paramount; we cannot deploy these systems at scale unless we are rigorously assured of their safety. Despite the capabilities of modern robotics, practical real-world safety is often achieved through conservative hardware designs, confining deployment regulations, or restrictive assumptions that severely limit a robot's capabilities.
\r\n\r\nThe goal of this thesis is to develop methods for achieving dynamic safety: formal safety guarantees that preserve system performance and remain valid under uncertainty. To this end, this thesis advances the theory and practice of control barrier functions (CBFs), a leading framework for enforcing safety constraints on dynamical systems. While CBF-based methods offer strong theoretical guarantees, they do so by relying on several restrictive assumptions. Namely, they assume that the safety requirement and the system dynamics are compatible and that the dynamics model and state are perfectly known. These assumptions rarely hold in real-world settings and can result in false confidence and catastrophic safety failures when violated. This thesis addresses these gaps by systematically relaxing these assumptions and developing new theory to retain rigorous, deployable guarantees.
\r\n\r\nBy leveraging structural properties of several relevant classes of system dynamics, I first present a myriad of constructive synthesis methods that make CBF design feasible for a wide range of robots. I then develop robust control methods that retain their safety guarantees in the presence of bounded dynamics and measurement uncertainty. However, despite the utility of these methods in guaranteeing safety, they often lead to highly conservative behavior that compromises system performance. Thus, to mitigate this conservatism, I leverage machine learning techniques to reduce uncertainty and determine desired levels of robustness. While this unification of machine learning techniques with safety-critical control may sacrifice formal guarantees, it enables safe and performant behavior in practice. Moreover, the robust CBF framework provides a valuable degree of interpretability absent from typical end-to-end approaches.
\r\n\r\nNext, seeking a middle ground between conservative absolute guarantees and capable-but-heuristic methods, I adopt a probabilistic notion of safety that provides risk-based guarantees in the presence of unbounded disturbances. In particular, by illustrating the fundamental connection between DCBFs and supermartingales, I develop new theoretical guarantees and propose several algorithms to achieve safety in the presence of stochastic uncertainty. I then deploy these methods on several complex systems experiencing significant uncertainty, including a quadrotor robot with a slung payload, a humanoid robot walking in unstructured environments, and multiple robots performing dynamic collision avoidance. To achieve this, we use generative modeling techniques to capture the necessary understanding of the uncertainty distribution. Here, I also forego the traditional CBF-based safety filter paradigm and show the performance and safety improvements that can be gained through the unification of CBFs and horizon-based methods such as model predictive control (MPC).
\r\n\r\nTogether, the contributions of this thesis represent an advancement towards dynamic, safe, and capable robotic autonomy under uncertainty. The risk-aware, robust safety-critical control methods proposed here help close the gap between theoretical safety guarantees and the demands of real-world deployment.
", "doi": "10.7907/eee7-0m74", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" } ]