Telerobotics fundamentally aims to project human skills into a remote, unstructured environment. A key component of human skills is anticipatory modulation of limb impedances in accordance with task requirements and in expectation of events or disturbances. These adjustments occur continually in human interaction strategies, yet are mostly masked in telerobotics by limited bandwidth controllers and fixed impedance hardware. We propose a telerobotic architecture with user-controlled variable impedance and show a single degree of freedom experimental implementation. The master incorporates a grip force sensor as an additional impedance command channel. Since grip force correlates with the user's own impedance, this input provides an intuitive and natural extension to the regular interface. On the slave, a physically variable clutch actuator is used to adjust both low and high frequency impedance. The additional command channel allows the operator to utilize impedance variation strategies to control impact forces and accomplish varying tasks. These natural interaction strategies are simpler and more robust, leading to superior performance and a telerobot which more effectively represents the operator.

Spring drive amplifiers utilize the natural inductive properties of a DC motor to produce stronger position feedback than is achievable with traditional current amplifiers. Impedance-type haptics devices can leverage these amplifiers to render superior contacts with higher contact stiffness. Use of these amplifiers, however, requires the virtual environment to specify motion commands for each motor. This work extends the recently developed 1-DOF integrated approach to multi-DOF applications. It presents a motion controller working with spring drive amplifiers, together producing the maximum achievable isotropic Cartesian stiffness. Isotropy is necessary to guarantee that haptic contact forces are rendered in the correct direction. The system is implemented on a PHANTOM 1.0 haptic device and analytic performance is verified by experiment.

This paper proposes an efficient technique for context-sensitive pointer analysis that is applicable to real C programs. For efficiency, we summarize the effects of procedures using partial transfer functions. A partial transfer function (PTF) describes the behavior of a procedure assuming that certain alias relationships hold when it is called. We can reuse a PTF in many calling contexts as long as the aliases among the inputs to the procedure are the same. Our empirical results demonstrate that this technique is successful—a single PTF per procedure is usually sufficient to obtain completely context-sensitive results. Because many C programs use features such as type casts and pointer arithmetic to circumvent the high-level type system, our algorithm is based on a low-level representation of memory locations that safely handles all the features of C. We have implemented our algorithm in the SUIF compiler system and we show that it runs efficiently for a set of C benchmarks.

This paper discusses three techniques useful in relaxing the constraints imposed by control flow on parallelism: control dependence analysis, executing multiple flows of control simultaneously, and speculative execution. We evaluate these techniques by using trace simulations to find the limits of parallelism for machines that employ different combinations of these techniques. We have three major results. First, local regions of code have limited parallelism, and control dependence analysis is useful in extracting global parallelism from different parts of a program. Second, a superscalar processor is fundamentally limited because it cannot execute independent regions of code concurrently. Higher performance can be obtained with machines, such as multiprocessors and dataflow machines, that can simultaneously follow multiple flows of control. Finally, without speculative execution to allow instructions to execute before their control dependences are resolved, only modest amounts of parallelism can be obtained for programs with complex control flow.