Diffusion strategies have advanced visual motor control by progressively denoising high-dimensional action sequences, providing a promising method for robot manipulation. However, as task complexity increases, the success rate of existing baseline models decreases considerably. Analysis indicates that current diffusion strategies are confronted with two limitations. First, these strategies only rely on short-term observations as conditions. Second, the training objective remains limited to a single denoising loss, which leads to error accumulation and causes grasping deviations. To address these limitations, this paper proposes Foresight-Conditioned Diffusion (ForeDiffusion), by injecting the predicted future view representation into the diffusion process. As a result, the policy is guided to be forward-looking, enabling it to correct trajectory deviations. Following this design, ForeDiffusion employs a dual loss mechanism, combining the traditional denoising loss and the consistency loss of future observations, to achieve the unified optimization. Extensive evaluation on the Adroit suite and the MetaWorld benchmark demonstrates that ForeDiffusion achieves an average success rate of 80% for the overall task, significantly outperforming the existing mainstream diffusion methods by approximately 20% in high difficulty tasks, while maintaining more stable performance across the entire tasks.

/sup A/ new approach to handle inductance effects for multiple signal lines is presented. The worst-case switching pattern is first identified. Then a numerical approach is used to model the effective loop inductance (L/sub eff/) for multiple lines. Based on a look-up table for L/sub eff/, an equivalent single line model can be generated to decouple a specific signal line from the others to perform static timing analysis. Compared to the use of full RLC netlists for multiple lines, this approach greatly improves the computational efficiency and maintains accuracy for timing and signal integrity analysis. We apply these models to repeater insertion in critical paths and find that, for a single line, the RLC model minimizes delay with fewer number of repeaters than RC model. However, for multiple lines, we find that same number of repeaters is inserted for optimal delay according to both the RC and RLC models.

In this paper we extend the O-tree approach to handle rectilinear blocks. First we explore the properties of L-shaped blocks, then decompose rectilinear blocks into a set of sub-L-shaped-blocks. The properties of L-shaped blocks can be applied to general recti?linear blocks. In order to explore the optimal search thoroughly, we generate four direction O-trees. A heuristic optimization algorithm based on the four direction O-trees produces very good experiment results.

In this paper we present an efficient yet accurate inductance extraction methodology and also apply it to clocktree RLC extraction. We first show that without loss of accuracy, the inductance extraction problem of n traces with or without ground planes can be reduced to a number of one-trace and two-trace subproblems. We then solve one-trace and two-trace subproblems via a table-based approach. We finally validate the linear cascading assumption that enables us to apply our inductance extraction approach to clocktree RLC extraction and optimization.