A humanoid robot pushing model inspired by human motion

This thesis explores an observed method used by humans when pushing a large object of unknown mass. Body motion and reaction forces are analyzed for feet-apart pushing with varying stance length. It is found that, via articulation of the waist, a human will push their static zero-moment point (ZMP) as far forward as possible prior to pushing. Along with an extended back leg, this provides a larger support region in which the ZMP can move before stability is lost. Using this motion, the subject can produce a larger force than if the waist is constrained. Further, in this stance the subject is stable without object contact and can exert a range of forces by controlling mass distribution at the feet. For this increases in force exertion and stability, a linearized double inverted pendulum model with a feet-apart stance is proposed for use in the humanoid robot pushing of an unknown mass. Using the human pushing data and our humanoid, HUBO+, the advantage of this model and the added degree of freedom is shown against the commonly used single inverted pendulum model for humanoid robot pushing.M.S., Mechanical Engineering and Mechanics -- Drexel University, 201

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC

Radiation-induced neoantigens broaden the immunotherapeutic window of cancers with low mutational loads

Immunotherapies are a promising advance in cancer treatment. However, because only a subset of cancer patients benefits from these treatments it is important to find mechanisms that will broaden the responding patient population. Generally, tumors with high mutational burdens have the potential to express greater numbers of mutant neoantigens. As neoantigens can be targets of protective adaptive immunity, highly mutated tumors are more responsive to immunotherapy. Given that external beam radiation 1) is a standard-of-care cancer therapy, 2) induces expression of mutant proteins and potentially mutant neoantigens in treated cells, and 3) has been shown to synergize clinically with immune checkpoint therapy (ICT), we hypothesized that at least one mechanism of this synergy was the generation of de novo mutant neoantigen targets in irradiated cells. Herein, we use KrasG12D x p53−/− sarcoma cell lines (KP sarcomas) that we and others have shown to be nearly devoid of mutations, are poorly antigenic, are not controlled by ICT, and do not induce a protective antitumor memory response. However, following one in vitro dose of 4- or 9-Gy irradiation, KP sarcoma cells acquire mutational neoantigens and become sensitive to ICT in vivo in a T cell-dependent manner. We further demonstrate that some of the radiation-induced mutations generate cytotoxic CD8+ T cell responses, are protective in a vaccine model, and are sufficient to make the parental KP sarcoma line susceptible to ICT. These results provide a proof of concept that induction of new antigenic targets in irradiated tumor cells represents an additional mechanism explaining the clinical findings of the synergy between radiation and immunotherapy.</jats:p