In this work, we study the problem of list decoding of insertions and deletions. We present a Johnson-type upper bound on the maximum list size. The bound is meaningful only when insertions occur. Our bound implies that there are binary codes of rate Ω(1) that are list-decodable from a 0.707-fraction of insertions. For any τI≥ 0 and τD∈ [0, 1), there exist q-ary codes of rate Ω(1) that are list-decodable from a τI-fraction of insertions and τI-fraction of deletions, where q depends only on τIand τ . We also provide efficient encoding and decoding algorithms for list-decoding from τI-fraction of insertions and τ -fraction of deletions for any τI≥ 0 and τD∈ [0, 1). Based on the Johnson-type bound, we derive a Plotkin-type upper bound on the code size in the Levenshtein metric.

List decoding of insertions and deletions is studied. A Johnson-type upper bound on the maximum list size is derived. The bound is meaningful only when insertions occurred. The result implies that there are binary codes that are potentially list-decodable from a 0.707-fraction of insertions in polynomial time. For non-binary code, for any constant r > 0, there are codes over constant-sized alphabets that achieve a constant list size for a list-decoding radius that is r times larger than the code length. Based on the Johnson-type bound, a Plotkin-type upper bound on the code size in the Levenshtein metric is also derived.

For assisting human motion, assistive devices working as muscles would be useful. A robot suit HAL (hybrid assistive limb) has been developed as an assistive device for lower limbs. Human can appropriately produce muscle contraction torque and control joint viscoelasticity by muscle effort such as co-contraction. Thus, to implement functions equivalent to human muscles using HAL, it is necessary to control viscoelasticity of HAL as well as to produce torque in accordance with operator's intention. Therefore the purpose of this study is to propose a control method of HAL using biological and motion information. In this method, HAL produces torque corresponding to muscle contraction torque by referring to the myoelectricity that is biological information to control operator's muscles. In addition, the viscoelasticities of HAL are adjusted in proportion to operator's viscoelasticity that is estimated from motion information by using an on-line parameter identification method. To evaluate the effectiveness of the proposed method, the method was applied to a swinging motion of a lower leg. When this method was applied, HAL could work like operator's muscles in the swinging motion, and as a consequence, the muscle activities of the operator were reduced. As a result of this experiment, we confirmed the effectiveness of the proposed method.

This paper proposes an algorithm to estimate human intentions during walking. Not only walk start or stop but walking cycle is considered as the intentions in this paper. The algorithm is embedded into a walking support system, a wearable robot "Robot Suit HAL-3", for paraplegia patients. The estimation of patients' intentions is indispensable for effective and comfortable motion support, but the biological signals such as myoelectricity which is used for the support by HAL-3 cannot be measured properly. The proposed algorithm, therefore, estimates patients' intentions from other channels such as a floor reaction force and a body posture. The effectiveness of this algorithm is investigated through experiments with two types of patients. One has a sensory paralysis on both legs, especially a left leg has severe trouble. The other has troubles in sensory and motor ability on both legs. We show HAL-3 supports patients' walk comfortably, estimating patient intentions.

We propose a dance partner robot referred to as Ms DanceR (Mobile Smart Dance Robot), which has been developed as platform for realizing the effective human-robot coordination with physical interaction. Ms DanceR consists of an omni-directional mobile base and a Body Force Sensor, which is a force/torque sensor installed between the mobile base and the body of the robot. A human could dance a ballroom dance together with Ms DanceR based on a control architecture referred to as "CAST" (Control Architecture based-on Step Transition), which was designed according to features of ballroom dances.