Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis

This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient to estimate an accurate reward function. Observing that each demonstrator has an inherent reward for each state and the task-specific behaviors mainly depend on a small number of key states, we propose a meta IRL algorithm that first models the reward function for each task as a distribution conditioned on a baseline reward function shared by all tasks and dependent only on the demonstrator, and then finds the most likely reward function in the distribution that explains the task-specific behaviors. We test the method in a simulated environment on path planning tasks with limited demonstrations, and show that the accuracy of the learned reward function is significantly improved. We also apply the method to analyze the motion of a patient under rehabilitation.Comment: arXiv admin note: text overlap with arXiv:1707.0939

Inverse Reinforcement Learning in Large State Spaces via Function Approximation

This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state spaces efficiently. We test the proposed method in a simulated environment, and show that it is more accurate than existing methods and significantly better in scalability. We also show that the proposed method can extend many existing methods to high-dimensional state spaces. We then apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.Comment: Experiment update

Plasmon geometric phase and plasmon Hall shift

The collective plasmonic modes of a metal comprise a pattern of charge density and tightly-bound electric fields that oscillate in lock-step to yield enhanced light-matter interaction. Here we show that metals with non-zero Hall conductivity host plasmons with a fine internal structure: they are characterized by a current density configuration that sharply departs from that of ordinary zero Hall conductivity metals. This non-trivial internal structure dramatically enriches the dynamics of plasmon propagation, enabling plasmon wavepackets to acquire geometric phases as they scatter. Strikingly, at boundaries these phases accumulate allowing plasmon waves that reflect off to experience a non-reciprocal parallel shift along the boundary displacing the incident and reflected plasmon trajectories. This plasmon Hall shift, tunable by Hall conductivity as well as plasmon wavelength, displays the chirality of the plasmon's current distribution and can be probed by near-field photonics techniques. Anomalous plasmon dynamics provide a real-space window into the inner structure of plasmon bands, as well as new means for directing plasmonic beams

Large optical conductivity of Dirac semimetal Fermi arc surfaces states

Fermi arc surface states, a hallmark of topological Dirac semimetals, can host carriers that exhibit unusual dynamics distinct from that of their parent bulk. Here we find that Fermi arc carriers in intrinsic Dirac semimetals possess a strong and anisotropic light matter interaction. This is characterized by a large Fermi arc optical conductivity when light is polarized transverse to the Fermi arc; when light is polarized along the Fermi arc, Fermi arc optical conductivity is significantly muted. The large surface spectral weight is locked to the wide separation between Dirac nodes and persists as a large Drude weight of Fermi arc carriers when the system is doped. As a result, large and anisotropic Fermi arc conductivity provides a novel means of optically interrogating the topological surfaces states of Dirac semimetals.Comment: 8 pages, 3 figure

Lifting the burden: fundamental tax reform and U.S. economic growth

This paper presents a comprehensive treatment of the cost-of-capital approach for analyzing the economic impact of tax policy. This approach has provided an intellectual impetus for reforms of capital income taxation in the United States and around the world. The most dramatic example is the Tax Reform Act of 1986 in the United States. In this landmark legislation the income tax base was broadened by wholesale elimination of tax preferences for both individuals and corporations. Revenues generated by base broadening were used to finance sharp reductions in tax rates at corporate and individual levels. The cost-of-capital approach presented in this paper shows that important opportunities for tax reform still remain. This approach suggests two avenues for reform. One would retain the income tax base of the existing U.S. tax system, but would equalize tax burdens on all forms of assets as well as average and marginal rates on labor income. Elimination of differences in the tax treatment of all forms of assets would produce gains in efficiency comparable to those from the Tax Reform Act of 1986. Equalization of marginal and average tax rates on labor income would more than double these gains in efficiency. Proposals to replace income by consumption as a tax base were revived in the United States during the 1990's. The Hall-Rabushka Flat Tax proposal would produce efficiency gains comparable to those from equalizing tax burdens on all forms of assets under the income tax. However, a progressive National Retail Sales Tax, collected on personal consumption expenditures at the retail level, would generate gains in efficiency exceeding those from the Flat Tax by more than 50 percent! Equalizing marginal and average rates of taxation on consumption would double the gains from the Flat Tax.

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings