Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis

Protecting the stars of tomorrow: do international cardiovascular preparticipation screening policies account for the paediatric athlete? A systematic review and quality appraisal.

This is the author accepted manuscript. The final version is available from BMJ Publishing Group via the DOI in this record OBJECTIVE: (1) Identify and review current policies for the cardiovascular screening of athletes to assess their applicability to the paediatric population and (2) evaluate the quality of these policy documents using the Appraisal of Guidelines for Research & Evaluation II (AGREE II) tool. DESIGN: Systematic review and quality appraisal of policy documents. DATA SOURCES: A systematic search of PubMed, MEDLINE, Scopus, Web of Science, SportDiscus and CINAHL. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: An article was included if it was a policy/position statement/guideline/consensus or recommendation paper relating to athletes and cardiovascular preparticipation screening. RESULTS AND SUMMARY: Of the 1630 articles screened, 13 met the inclusion criteria. Relevance to paediatric athletes was found to be high in 3 (23%), moderate in 6 (46%) and low in 4 (31%), and only 2 provide tailored guidance for the athlete aged 12-18 years. A median 5 related citations per policy investigated solely paediatric athletes, with study designs most commonly being retrospective (72%). AGREEII overall quality scores ranged from 25% to 92%, with a median of 75%. The lowest scoring domains were rigour of development; (median 32%) stakeholder involvement (median 47%) and Applicability (median 52%). CONCLUSION: Cardiac screening policies for athletes predominantly focus on adults, with few providing specific recommendations for paediatric athletes. The overall quality of the policies was moderate, with more recent documents scoring higher. Future research is needed in paediatric athletes to inform and develop cardiac screening guidelines, to improve the cardiac care of youth athletes

Primary repair versus surgical and transcatheter palliation in infants with tetralogy of Fallot

Objectives Treatment of infants with tetralogy of Fallot (ToF) has evolved in the last two decades with increasing use of primary surgical repair (PrR) and transcatheter right ventricular outflow tract palliation (RVOTd), and fewer systemic-to-pulmonary shunts (SPS). We aim to report contemporary results using these treatment options in a comparative study. Methods This a retrospective study using data from the UK National Congenital Heart Disease Audit. All infants (n=1662, median age 181 days) with ToF and no other complex defects undergoing repair or palliation between 2000 and 2013 were considered. Matching algorithms were used to minimise confounding due to lower age and weight in those palliated. Results Patients underwent PrR (n=1244), SPS (n=311) or RVOTd (n=107). Mortality at 12 years was higher when repair or palliation was performed before the age of 60 days rather than after, most significantly for primary repair (18.7% vs 2.2%, P<0.001), less so for RVOTd (10.8% vs 0%, P=0.06) or SPS (12.4% vs 8.3%, P=0.2). In the matched groups of patients, RVOTd was associated with more right ventricular outflow tract (RVOT) reinterventions (HR=2.3, P=0.05 vs PrR, HR=7.2, P=0.001 vs SPS) and fewer pulmonary valve replacements (PVR) (HR=0.3 vs PrR, P=0.05) at 12 years, with lower mortality after complete repair (HR=0.2 versus PrR, P=0.09). Conclusions We found that RVOTd was associated with more RVOT reinterventions, fewer PVR and fewer deaths when compared with PrR in comparable, young infants, especially so in those under 60 days at the time of the first procedure